Ehsan Hoque from the University of Rochester gave a talk at MBZUAI discussing how to integrate AI into healthcare to improve access and equity. He emphasized that technology should align with values and infrastructure, advocating for AI solutions developed through collaboration between computer scientists and healthcare professionals. Hoque presented examples like using AI to quantify movement disorders and improve empathy skills. Why it matters: This highlights the importance of human-centered AI development in the GCC region, particularly in sensitive sectors like healthcare, and MBZUAI's role in fostering such discussions.
A PhD candidate from the University of Waterloo presented on threats from large machine learning systems at MBZUAI. The talk covered data privacy during inference and the misuse of ML systems to generate deepfakes. The speaker also analyzed differential privacy and watermarking as potential solutions. Why it matters: Understanding and mitigating the risks of large ML systems is crucial for responsible AI development and deployment in the region.
Patrick van der Smagt, Director of AI Research at Volkswagen Group, discussed the use of generative machine learning models for predicting and controlling complex stochastic systems in robotics. The talk highlighted examples in robotics and beyond and addressed the challenges of achieving quality and trust in AI systems. He also mentioned his involvement in a European industry initiative on trust in AI and his membership in the AI Council of the State of Bavaria. Why it matters: Understanding control in robotics, along with trust in AI, are key issues for further development of autonomous systems, especially in industrial applications within the GCC region.
Sai Praneeth Karimireddy from UC Berkeley presented a talk on building planetary-scale collaborative intelligence, highlighting the challenges of using distributed data in machine learning due to data silos and ethical-legal restrictions. He proposed collaborative systems like federated learning as a solution to bring together distributed data while respecting privacy. The talk addressed the need for efficiency, reliability, and management of divergent goals in these systems, suggesting the use of tools from optimization, statistics, and economics. Why it matters: Collaborative AI systems can unlock valuable distributed data in the region, especially in sensitive sectors like healthcare, while ensuring privacy and addressing ethical concerns.
The article discusses the potential of AI-enabled assistive technologies to empower People with Disabilities (PWD), citing that over one billion people live with some form of disability globally. It highlights examples like communication tools, assistive robots, and smart visual aids, and emphasizes the need to address security and privacy concerns. The author, Ishfaq Ahmad from the University of Texas at Arlington, points out that with a growing global population, over two billion people will need assistive products by 2030. Why it matters: The piece advocates for using AI to tackle critical human rights issues and improve the lives of a significant portion of the global population in the face of increasing disability rates.
This article discusses domain shift in machine learning, where testing data differs from training data, and methods to mitigate it via domain adaptation and generalization. Domain adaptation uses labeled source data and unlabeled target data. Domain generalization uses labeled data from single or multiple source domains to generalize to unseen target domains. Why it matters: Research in mitigating domain shift enhances the robustness and applicability of AI models in diverse real-world scenarios.
A CMU professor and MBZUAI affiliated faculty presented research on how LLMs store and use knowledge learned during pre-training. The study used a synthetic biography dataset to show that LLMs may not effectively use memorized knowledge at inference time, even with zero training loss. Data augmentation during pre-training can force the model to store knowledge in specific token embeddings. Why it matters: The research highlights limitations in LLM knowledge manipulation and extraction, with implications for improving model architectures and training strategies for more effective knowledge utilization in Arabic LLMs.
Christian Montag from Ulm University gave a talk about assessing attitudes towards AI, covering the IMPACT framework (Modality, Person, Area, Country/Culture, and Transparency). He discussed how factors like age, gender, personality, and culture relate to attitudes toward AI, and how those attitudes link to trust in automation and specific AI models like ChatGPT and Ernie Bot. Montag's research explores the intersection of psychology, neuroscience, behavioral economics, and computer science, focusing on the impact of AI on the human mind. Why it matters: Understanding public perception of AI is crucial for responsible development and deployment, especially in the Arab world where cultural and demographic factors can significantly shape attitudes.
The InterText project, funded by the European Research Council, aims to advance NLP by developing a framework for modeling fine-grained relationships between texts. This approach enables tracing the origin and evolution of texts and ideas. Iryna Gurevych from the Technical University of Darmstadt presented the intertextual approach to NLP, covering data modeling, representation learning, and practical applications. Why it matters: This research could enable a new generation of AI applications for text work and critical reading, with potential applications in collaborative knowledge construction and document revision assistance.
MBZUAI alumnus Ahmed Sharshar is developing smaller AI models to make the technology more accessible, especially in resource-constrained environments like Egypt. His master's thesis involved creating an app that assesses lung health using mobile phone video analysis, eliminating the need for traditional medical devices. Sharshar is pursuing his Ph.D. at MBZUAI, focusing on lightweight and energy-efficient models for various applications. Why it matters: Democratizing AI through smaller, efficient models can enable broader applications and innovation across diverse sectors in the Middle East and beyond.
A panel discussion was hosted at MBZUAI in collaboration with the Manara Center for Coexistence and Dialogue. The discussion centered on the potential of multimodal machine intelligence for human-centered applications, particularly in health and wellbeing. USC Professor Shrikanth Narayanan spoke on creating trustworthy and inclusive AI that considers protected variables. Why it matters: This signals MBZUAI's interest in exploring ethical AI development and its applications for societal good, potentially driving research and policy initiatives in the region.
Justice Connect, an Australian charity, collaborated with MBZUAI's Prof. Timothy Baldwin to improve their legal intake tool using NLP. The tool helps route legal requests, but users struggled to identify the relevant area of law, leading to delays and frustration. By applying NLP, the collaboration aims to help users more easily navigate the tool and access appropriate legal resources. Why it matters: This project demonstrates how NLP can be applied to improve access to justice and address unmet legal needs, particularly for those unfamiliar with legal terminology.
MBZUAI researchers presented new resources at EMNLP for improving the factuality of LLMs, including a web application for fact-checking LLM-generated text and benchmarks for evaluating automated fact-checkers. They found that current automated fact-checkers miss nearly 40% of false claims generated by LLMs. The study breaks down the fact-checking process into eight tasks, including decomposition and decontextualization, to identify where systems fail. Why it matters: This work addresses a critical challenge in the deployment of LLMs by providing tools and methods for improving their reliability and trustworthiness, which is essential for widespread adoption in sensitive applications.
Researchers from MBZUAI, University of Washington, and other institutions presented studies at EMNLP 2024 exploring how LLMs represent cultures. A survey analyzed dozens of recent studies on LLMs and culture and proposes a new framework for future research. The survey found that there is no widely accepted definition of 'culture' in NLP, making it challenging to interpret how models represent culture through language. Why it matters: This highlights a key gap in the field and emphasizes the need for a more rigorous and consistent understanding of culture in AI, especially as LLMs become more globally integrated.
Researchers from MBZUAI and Monash University presented a study at EMNLP 2024 examining LLMs' ability to interpret empathy, emotion, and morality in written stories. The study builds on a framework for modeling empathic similarity between narratives, using the EmpathicStories dataset. They are exploring ways to improve LLMs' capabilities with complex concepts like empathy, especially for applications in fields like healthcare. Why it matters: Enhancing LLMs with empathic understanding could lead to more effective and human-centered AI applications, particularly in sensitive domains requiring nuanced communication.
MBZUAI researchers developed Human-in-the-Loop for Prognosis (HuLP), a new AI system designed to help physicians assess cancer progression by providing information about its predictions and allowing user intervention. The system aims to foster collaboration between physicians and AI, rather than replacing doctors. It was presented at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Why it matters: This research highlights the potential of AI to augment physician expertise in critical areas like cancer prognosis, improving patient care and treatment decisions.
Freddy Jimenez, regional innovation lead for Pfizer in the Middle East, Africa, and Russia, is working to implement AI solutions for healthcare challenges in the UAE. Jimenez joined the Executive Education Program at MBZUAI to expand his knowledge and drive the country's healthcare industry forward. He emphasizes the importance of strategic approaches to AI in healthcare due to rapid technological advancements outpacing regulation. Why it matters: This highlights the UAE's focus on leveraging AI to improve healthcare outcomes and the importance of collaboration between industry, academia, and government to accelerate AI adoption in the heavily regulated healthcare sector.
MBZUAI's computer science department, led by Xiaosong Ma, focuses on improving AI efficiency and sustainability by reducing wasted resources. Xiaosong's background in high-performance computing informs her approach to optimizing AI workloads. She aims to collaborate with experts across different AI domains at MBZUAI to address these challenges. Why it matters: Optimizing AI efficiency is crucial for reducing the environmental impact and computational costs associated with increasingly complex AI models in the GCC region and globally.
MBZUAI alumnus Sulaiman AlJanahi is helping reshape the UAE's payments landscape as a founding member and associate project manager at Al Etihad Payments, a subsidiary of the Central Bank of the UAE. AlJanahi's work focuses on the Aani instant payment system, building on his M.Sc. research on optimization techniques for federated learning. He credits MBZUAI for instilling discipline and a strong work ethic, preparing him for the demands of a fast-paced startup environment. Why it matters: This highlights MBZUAI's role in contributing to the UAE's digital transformation and cashless society initiatives through its graduates' involvement in key national projects.
MBZUAI alumnus Adnan Khan is pursuing a Ph.D. at Carleton University, focusing his research on using computer vision to improve accessibility in healthcare, particularly for the visually impaired. His work builds upon his master's thesis at MBZUAI, which focused on domain generalization, enabling models to adapt across different data domains. Khan credits his experiences at MBZUAI for shaping his community spirit and career path. Why it matters: This highlights the role of AI education in fostering socially impactful research and driving innovation in healthcare accessibility in the region and beyond.
MBZUAI researchers are studying how AI can be used to combat disinformation and improve news verification during elections, as AI amplifies the volume and speed of fake news. Dilshod Azizov is using machine learning to spot patterns in news that will improve verification, while Preslav Nakov's FRAPPE system identifies persuasive techniques and framing in news articles. FRAPPE uses machine learning and NLP to analyze news presentation and reporting, aiming to help users understand the underlying context of news. Why it matters: This research highlights the potential of AI to both negatively and positively impact democratic processes, emphasizing the need for tools to analyze and verify information in the face of increasing AI-generated disinformation.
Researchers from MBZUAI and other institutions presented a study at ACL 2024 on combatting misinformation by identifying misrepresented scientific research. They compiled a dataset called MISSCI, comprised of real-world examples of misinformation gathered from the HealthFeedback fact-checking website. The annotators classified the different types of errors in reasoning into nine different classes. Why it matters: This work addresses a critical need to combat the spread of scientific falsehoods online, especially given the challenges of manual fact-checking.
MBZUAI highlighted five Emirati women making contributions in AI, entrepreneurship, and leadership, coinciding with Emirati Women’s Day. Fatima AlKhoori is pursuing a Ph.D. at MBZUAI, researching transformer models for autonomous vehicle traffic sign recognition using UAE and German datasets. Dr. Hanan Aldarmaki, an MBZUAI assistant professor, specializes in Arabic speech and language processing. Why it matters: Showcasing Emirati women in AI underscores the UAE's commitment to gender diversity and national talent development in advanced technology fields.
MBZUAI researchers have expanded LLM safety research to Chinese, presenting their work at the 62nd Annual Meeting of the Association for Computational Linguistics in Bangkok. They developed an open-source Chinese dataset of 3,000 prompts translated and localized from the English "Do-Not-Answer" dataset. The dataset includes a "region-specific sensitivity" category to address unique safety risks for Chinese speakers, evaluating if models are over-sensitive in identifying innocuous questions as harmful. Why it matters: This research addresses a critical gap in LLM safety evaluation, ensuring that language models are both safe and effective for diverse linguistic and cultural contexts, particularly in regions with unique sensitivities.
MBZUAI researchers presented a new uncertainty quantification method at ACL to identify hallucinations in LLMs, called claim conditioned probability (CCP). CCP leverages the internal token probabilities generated by the LLM itself to highlight claims with low confidence. Unlike external fact-checking methods, CCP is computationally efficient as it uses probabilities already computed by the model. Why it matters: This research offers a practical approach to mitigate the impact of LLM hallucinations by highlighting potentially unreliable information, improving the trustworthiness of these models, especially for Arabic LLMs.
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) emphasizes campus life to support its growing international student population. The Masdar City campus provides living quarters, communal spaces, and services for student wellbeing. MBZUAI offers advanced AI labs with nearly 1,000 Nvidia GPU servers and high-performance computing resources, along with technology-enhanced classrooms and a well-stocked library. Why it matters: This focus on comprehensive campus resources signals MBZUAI's commitment to attracting and supporting top AI talent in the UAE.
MBZUAI researchers presented a study at ICML 2024 examining how data aggregation distorts causal discovery. The study argues that current methods are misled because real-world interactions happen at a micro level while observations are aggregated. Using the example of ice cream sales and temperature, they highlight how aggregation introduces "instantaneous causality" where time-lags exist. Why it matters: The research identifies a fundamental limitation in current causal discovery methods, potentially impacting disciplines relying on accurate causal inference from observational data.
MBZUAI's first Ph.D. graduate, Numan Saeed, developed deep learning models to diagnose head and neck cancers using PET and CT scan imagery. His research focused on improving early detection and accurate localization of tumors, aiming to enhance diagnosis and prognosis. Early diagnosis can reduce mortality rates by up to 70%. Why it matters: This research showcases the potential of AI in healthcare to improve cancer diagnosis and treatment, addressing a critical need in resource-constrained healthcare systems.
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 President delivered an address to the Class of 2024 at the Abu Dhabi Energy Center. He congratulated the graduates and highlighted the importance of AI as a transformative technology, comparing it to the printing press in its potential impact. He urged graduates to bring AI to the people and people to AI, emphasizing the need for understanding and safe steering of this powerful technology. Why it matters: This speech signals MBZUAI's continued emphasis on the societal impact and ethical considerations surrounding AI as it trains the next generation of AI specialists in the region.
MBZUAI computer vision graduate Yahia Dalbah pivoted from electrical engineering to a role at FortyGuard, a clean tech startup focused on geospatial analysis of outdoor temperatures. He will be a research and development engineer, responsible for machine learning and computer vision algorithms. FortyGuard provides governments and businesses with tools to measure and analyze factors affecting heat and temperature. Why it matters: This highlights the potential for AI education in the UAE to drive innovation in sustainability and environmental solutions through local startups.
MBZUAI will graduate its first three Ph.D. students on June 6, 2024. Numan Saeed's research focused on using AI and big data to diagnose head and neck cancers using PET and CT scans, aided by NLP to interpret doctors' notes. The graduates will remain in the UAE to contribute to the country's growing AI sector. Why it matters: This milestone highlights the growing AI talent pool in the UAE and the potential for AI research at MBZUAI to address critical healthcare challenges.
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.
LibrAI is an AI startup founded by Xudong Han, a University of Melbourne PhD graduate, in December 2023 after the release of ChatGPT. The company focuses on advancing AI safety and responsible AI practices, building on Han's prior work creating FairLib, an open-source toolkit for fairness in deep neural networks. LibrAI's team of seven aims to create practical solutions ensuring AI is both responsible and revolutionary. Why it matters: The establishment of a startup focused on AI safety highlights growing awareness of ethical considerations in AI development within the region.
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.
MBZUAI Professor Preslav Nakov has developed FRAPPE, an interactive website that analyzes news articles to identify persuasion techniques. FRAPPE helps users understand framing, persuasion, and propaganda at an aggregate level, across different news outlets and countries. Presented at EACL, FRAPPE uses 23 specific techniques categorized into six broader buckets, such as 'attack on reputation' and 'manipulative wording'. Why it matters: The tool addresses the increasing difficulty in discerning factual information from disinformation, providing a means to identify biases in news media from different countries.
MBZUAI has announced an enhanced MBZUAI Executive Program (MEP) welcoming its fifth cohort of 44 senior leaders from various sectors across the UAE. The program focuses on AI application and implementation, covering real-world projects, policy development, and ethics. The in-person modules are spread over 16 weeks and include new chapters on AI transformation, policymaking, and ethics. Why it matters: The enhanced MEP helps develop a strong regional AI community, supporting digital transformation in UAE organizations and aligning with the UAE's national AI strategy.
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 researcher Karima Kadaoui is using AI to assist disadvantaged communities and languages, with a focus on democratizing NLP tasks for Arabic dialects. Her master's thesis focused on impaired speech recognition, converting disfluencies of individuals with speech disabilities into clear speech. She emphasizes the importance of diversity and inclusion in AI to avoid bias and ensure systems reflect the user distribution. Why it matters: This highlights MBZUAI's commitment to gender equity in STEM and the development of AI solutions tailored to the nuances of the Arabic language.
MBZUAI hosted the 2024 Middle East Advancement Conference (MEAC) in Abu Dhabi, gathering over 50 delegates from global educational institutions. The conference, organized by the Council for Advancement and Support of Education (CASE), focused on international best practices in areas like alumni relations and student recruitment. The event included discussions on the impact of AI on advancement professionals and featured leaders from MBZUAI, NYU Abu Dhabi, and Heriot-Watt University Dubai. Why it matters: The conference highlights the UAE's growing role as a hub for educational innovation and its commitment to fostering a globally networked approach to talent development in the AI era.
MBZUAI is hosting the third AI Quorum of the academic year, focusing on the future of human-computer interaction (HCI) in the age of AI. The event gathers researchers and practitioners from various disciplines and institutions, including University College London, Apple, and Google. The workshop aims to spur AI research and promote understanding of AI's potential for social good, with previous Quorums focusing on federated learning and statistics. Why it matters: This interdisciplinary focus on HCI at MBZUAI highlights the UAE's commitment to shaping the ethical and practical integration of AI into everyday life.
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 and the Weizmann Institute of Science (WIS) jointly hosted the inaugural Human Phenotype Project Hackathon in Abu Dhabi, focusing on AI-driven healthcare solutions. 24 postgraduate students and researchers analyzed "real data" from a deep-phenotype multi-omic biobank to develop personalized and predictive analytics. The hackathon utilized data from WIS and Pheno.AI’s Human Phenotype Project (HPP) in Israel, marking the first time an international university has been granted access to this data. Why it matters: This collaboration demonstrates the growing emphasis on leveraging AI for healthcare innovation in the UAE and fostering international partnerships to address global health challenges.
MBZUAI hosted the Second Workshop on Collaborative Learning as part of the AI Quorum in Abu Dhabi, focusing on collaborative and federated learning for sustainable development. Researchers discussed applications in medicine, biology, ecological conservation, and humanitarian aid. Eric Xing highlighted the potential of large biology models, similar to LLMs, to revolutionize biological data analysis. Why it matters: This workshop underscores the UAE's commitment to advancing AI research in crucial sectors like healthcare and sustainability through collaborative learning approaches.
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 awarded its first Ph.D. to Numan Saeed for his dissertation, "Deep Learning for Cancer Diagnosis and Prognosis." Saeed's research explores using deep learning models to diagnose tumors and predict survival rates for head and neck cancers by interpreting PET and CT scan imagery and doctor's notes. The AI model considers variables like age, gender, and tumor size to improve diagnosis, especially in regions with limited oncological services. Why it matters: This milestone highlights MBZUAI's growing role in AI research and its potential to contribute to advancements in AI-driven healthcare solutions, particularly for cancer diagnosis and treatment.
A team of MBZUAI graduate students won first place at the UAE University's University Challenge for their SawabAI project, which addresses AI-generated misinformation about climate change. The winning team included Salem Bin Saqer AlMarri, Hanoona Bangalath, and Muhammad Maaz, all Computer Vision Ph.D. students. SawabAI is envisioned as a platform to evaluate the authenticity and source of information, including text, image, and video, to combat fake news. Why it matters: This win highlights the growing importance of AI in addressing misinformation and promoting sustainability in government communication within the region.