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 Professor Fakherddine Karray is developing deep learning algorithms for human activity recognition to monitor the health and safety of elderly people. The AI tools analyze movement, posture, and facial expressions to detect early warning signs of health emergencies. Remote patient monitoring systems integrate smart devices and secure communication to allow elderly patients to stay at home and communicate with healthcare providers. Why it matters: AI-powered smart homes can provide affordable healthcare solutions for the rapidly growing elderly population in the region and worldwide.
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 Professor Preslav Nakov is researching methods to combat fake news and online disinformation through NLP techniques. His work focuses on detecting harmful memes and identifying the stance of individuals regarding disinformation. Four of Nakov’s recent papers on these topics were presented at NAACL 2022. Why it matters: This research aims to mitigate the impact of weaponized news and online manipulation, contributing to a more trustworthy information environment in the region and globally.
An MBZUAI team developed a self-ensembling vision transformer to enhance the security of AI in medical imaging. The model aims to protect patient anonymity and ensure the validity of medical image analysis. It addresses vulnerabilities where AI systems can be manipulated, leading to misinterpretations with potentially harmful consequences in healthcare. Why it matters: This research is crucial for building trust and enabling the safe deployment of AI in sensitive medical applications, protecting against fraud and ensuring patient safety.
An article highlights the role of AI in promoting female empowerment, particularly in the UAE, where Emirati women entrepreneurs constitute a significant portion of business owners. MBZUAI is playing a key role by equipping women with AI skills, as exemplified by alumna Farha Albreiki, who is applying her ML expertise at Abu Dhabi Transmission and Despatch Company (TRANSCO). Albreiki is also involved in initiatives like the TRANSCO Women Working Group to foster gender diversity in the tech sector. Why it matters: This underscores the importance of AI education and initiatives within the GCC to support women's participation and leadership in technology and engineering.
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'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 Board of Trustees member Anil K. Jain received the 2023 Technology Transfer Achievement Award from Michigan State University for his work in biometric recognition. Jain developed core algorithms for biometric pattern representation and search, licensing these technologies to industry. He also advised India's Aadhaar, the world's largest biometric ID system. Why it matters: This award highlights the importance of translating academic research into practical applications that impact society, particularly in the realm of secure identification and access.
MBZUAI researchers will present 20 papers at the 40th International Conference on Machine Learning (ICML) in Honolulu. Visiting Associate Professor Tongliang Liu leads with seven publications, followed by Kun Zhang with six. One paper investigates semi-supervised learning vs. model-based methods for noisy data annotation in deep neural networks. Why it matters: The research addresses the critical issue of data quality and accessibility in machine learning, particularly for organizations with limited resources for data annotation.
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 researchers are using federated learning to optimize energy production and use in microgrids, balancing individual and grid-level needs with a focus on sustainability. They presented a multi-agent framework called MAHTM at the ICLR 2023 workshop, aiming to minimize the carbon footprint of electrical grids. The system uses three layers of decision-making agents to minimize cost, decrease carbon impact, and balance production. Why it matters: This research offers a novel approach to integrating renewable energy sources into existing grids, potentially accelerating the transition to more sustainable energy systems in the region and globally.
MBZUAI students participated in the Climate Ambassadors Programme (CAP) at Expo City Dubai, role-playing a COP28 session. Representing different countries, the students developed innovative climate change solutions, emphasizing AI's role. The event, part of a series leading up to COP28, saw MBZUAI ambassadors highlighting the need for proactive environmental action. Why it matters: This initiative showcases the potential of AI-focused education in addressing global climate challenges and promoting youth engagement in sustainability efforts within the UAE.
MBZUAI President Eric Xing has been named a 2023 Fellow of the Institute of Mathematical Statistics (IMS). He was honored for contributions to statistics, machine learning research, AI entrepreneurship, and AI education. The IMS will formally recognize the 2023 fellows at the Joint Statistical Meetings in Toronto in August. Why it matters: This recognition highlights the growing prominence of MBZUAI and its leadership in the international AI and statistics community.
Emirates Health Services (EHS) and Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) have signed an MoU to strengthen cooperation and knowledge sharing. The agreement focuses on developing human capital, promoting skills, and advancing scientific research and innovation. It includes research opportunities, sponsoring EHS employees for master’s and doctorate degrees at MBZUAI, and providing specialized AI training programs in healthcare. Why it matters: This partnership aims to enhance healthcare efficiency and digital skills in the UAE through AI, contributing to the overall well-being of the population.
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.
MBZUAI researchers are working to improve computer vision models by incorporating common sense knowledge. They aim to address issues like the generation of unrealistic human features, such as hands with incorrect numbers of fingers. By integrating common-sense knowledge, like the fact that humans typically have five fingers per hand, they seek to make deep learning models more reliable. Why it matters: This research could improve the accuracy and trustworthiness of AI-generated content, making it more suitable for real-world applications.
Sheikha Shamma bint Sultan bin Khalifa Al Nahyan stated that AI is crucial for the UAE to achieve its Net Zero goals by 2050. Other Emirati officials who graduated from the MBZUAI Executive Programme also shared their views on AI applications in healthcare, government services, and energy. They highlighted AI's potential in disease diagnosis, digital transformation, and resource allocation. Why it matters: This underscores the UAE's commitment to leveraging AI across various sectors to address climate change and improve public services.
Bruno Ribeiro from Purdue University presented a talk on Asymmetry Learning and Out-of-Distribution (OOD) Robustness. The talk introduced Asymmetry Learning, a new paradigm that focuses on finding evidence of asymmetries in data to improve classifier performance in both in-distribution and out-of-distribution scenarios. Asymmetry Learning performs a causal structure search to find classifiers that perform well across different environments. Why it matters: This research addresses a key challenge in AI by proposing a novel approach to improve the reliability and generalization of classifiers in unseen environments, potentially leading to more robust AI systems.
The article discusses the challenges in effectively applying text classification techniques, despite the availability of tools like LibMultiLabel. It highlights the importance of guiding users to appropriately use machine learning methods due to considerations in practical applications such as evaluation criteria and data strategies. The piece also mentions a panel discussion hosted by MBZUAI in collaboration with the Manara Center for Coexistence and Dialogue. Why it matters: This signals ongoing efforts within the UAE AI ecosystem to address practical challenges and promote responsible AI usage in NLP applications.
Eran Segal from Weizmann Institute of Science presented The Human Phenotype Project, a large-scale prospective cohort with over 10,000 participants. The project aims to identify novel molecular markers and develop prediction models for disease onset using deep profiling. The profiling includes medical history, lifestyle, blood tests, and molecular profiling of the transcriptome, genetics, microbiome, metabolome and immune system. Why it matters: Such projects demonstrate the growing focus on personalized medicine in the region, utilizing advanced AI and machine learning techniques for disease prevention and treatment.
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 is developing the AI Operating System (AIOS) to reduce the energy, time, and talent costs of AI computing. AIOS aims to make AI models smaller, faster, and more efficient, reducing reliance on expensive hardware and speeding up compute operations. It also enables cost-aware model tuning and standardizes AI modules for reliable operation. Why it matters: By addressing the environmental impact and resource demands of AI, AIOS could promote more sustainable and accessible AI development in the region and globally.
MBZUAI and the Weizmann Institute of Science (WIS) are deepening research ties through ongoing engagement and knowledge sharing. A delegation from MBZUAI visited WIS to discuss collaboration across a range of research projects overseen by a joint review board. The partnership, formalized in 2021 through the MBZUAI-WIS Joint Program for Artificial Intelligence Research, focuses on advancing research in natural and machine intelligence. Why it matters: This collaboration strengthens the AI research ecosystem in the region and leverages expertise from both institutions to develop AI technologies for societal benefit.
MBZUAI's Women in AI (WAI) club, founded by master’s students Asma Hashmi and Ameera Bawazir, aims to increase female representation in AI at MBZUAI and the UAE. The club aligns with the International Day of Women and Girls in Science, addressing the underrepresentation of women in AI globally (22%). MBZUAI reports 31% female students in its first cohort and hopes to increase this, supported by faculty like Prof. Najwa Aaraj. Why it matters: This initiative highlights efforts to close the gender gap in AI within the UAE's leading AI university, fostering a more inclusive and diverse tech ecosystem.
Researchers at Johns Hopkins are developing AI-driven video analysis tools to provide surgeons with unbiased skill assessments and personalized feedback. The system segments surgical procedures, detects instruments, and assesses skill in cataract surgery. Dr. Shameema Sikder is leading the development of technologies to improve ophthalmic surgical care standards internationally. Why it matters: AI-based surgical skill assessment could standardize training and improve patient outcomes in the region and globally.
MBZUAI valedictorian Shahd AlShamsi is using AI and ML to develop personalized cognitive healthcare, shifting treatment from reaction to prevention. Her master's research involves a digital twin framework that integrates representations of a person’s cognitive experience using deep learning models and EEG data. She hopes to develop a mobile application to extend her work to personalized mental health. Why it matters: This research highlights the potential of AI to improve personalized healthcare in the UAE and beyond, and demonstrates the contributions of Emirati researchers.
MBZUAI's Metaverse Lab is developing AI algorithms for photorealistic virtual humans and dynamic environments. Hao Li, Director of the lab, envisions using the metaverse for immersive learning experiences related to history and culture. He is also working on tools to prevent deepfakes and other cyberthreats. Why it matters: This research at MBZUAI aims to advance AI and immersive technologies for education and address potential risks in the metaverse.
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 researchers are applying federated learning to optimize smart grids while protecting user data privacy. This approach leverages techniques from smart healthcare systems to enhance energy efficiency and local energy sharing. The research addresses the challenge of balancing grid optimization with the risk of user identity theft associated with traditional data-intensive smart grids. Why it matters: This research demonstrates a practical application of privacy-preserving AI in critical infrastructure, addressing key concerns around data security and fostering trust in smart grid technologies.
MBZUAI Professor Preslav Nakov believes AI can outpace human fact-checkers in detecting fake news by analyzing language and sentence structure. AI systems can identify common sources of fake news and flag domains for blocking. Nakov's research focuses on disinformation, fact checking, and media bias detection. Why it matters: AI-driven solutions for combating fake news could help mitigate the spread of misinformation and its impact on society, especially 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.
Gus Xia, assistant professor of machine learning at MBZUAI, is exploring how teaching robots music can enhance their interaction with humans. Xia collaborates with robots on musical compositions as part of this research. He also holds affiliations at NYU Shanghai, Tandon, CILVR, and MARL, and has a Ph.D. from CMU. Why it matters: This interdisciplinary approach could lead to more intuitive and empathetic AI systems in the future.
Hao Li, CEO of Pinscreen and associate professor at MBZUAI, discussed the Metaverse at GITEX 2022. Li's work focuses on virtual humans, reality capture, and AI synthesis to enable AI and immersive technologies. He aims to develop tools that prevent cyberthreats like deepfakes. Why it matters: This highlights the GCC's growing role in shaping the development and ethical considerations of metaverse technologies.
MBZUAI master's student Sayed Hashim is applying machine learning to improve cancer diagnosis and treatment, motivated by personal loss. He and fellow student Muhammad Ali developed algorithms for cancer type classification from multi-omics data, achieving over 96% accuracy. Their work, supervised by MBZUAI faculty, resulted in a published paper on multi-omics data representation learning. Why it matters: This research demonstrates the potential of AI and machine learning to advance cancer research and personalized medicine in the region.
Xi Chen from NYU Stern gave a talk at MBZUAI on digital privacy in personalized pricing using differential privacy. The talk also covered research in Web3 and decentralized finance, including delta hedging liquidity positions on Uniswap V3. Chen highlighted open problems in decentralized finance during the presentation. Why it matters: The talk suggests MBZUAI's interest in exploring the intersection of AI, privacy, and blockchain technologies, reflecting growing trends in data protection and decentralized systems.
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 Professor Kun Zhang's research focuses on causality in AI systems, aiming to understand underlying processes beyond data correlation. He emphasizes the importance of causality and graphical representations to model why systems produce observations and account for uncertainty. Zhang served as a program chair at the 38th Conference on Uncertainty in Artificial Intelligence (UAI) in Eindhoven. Why it matters: This highlights the growing importance of causality and uncertainty in AI research, crucial for responsible AI deployment and decision-making in the region.
MBZUAI Assistant Professor Samuel Horváth is researching federated learning to address the tension between data privacy and the predictive power of machine learning models. Federated learning trains models on decentralized data, keeping sensitive information on devices. Horváth's research focuses on designing algorithms that can efficiently train on distributed data while respecting user privacy. Why it matters: This work is crucial for advancing AI in sensitive domains like healthcare, where privacy regulations limit centralized data collection.
This article discusses adversarial training (AT) as a method to improve the robustness of machine learning models against adversarial attacks. AT aims to correctly classify data and ensure no data fall near decision boundaries, simulating adversarial attacks during training. Dr. Jingfeng Zhang from RIKEN-AIP will present on improvements to AT and its application in evaluating and enhancing the reliability of ML methods. Why it matters: As ML models become more prevalent in real-world applications in the GCC region, ensuring their robustness against adversarial attacks is crucial for maintaining their reliability and security.
Professor Hava Siegelmann, a computer science expert, is researching lifelong learning AI, drawing inspiration from the brain's abstraction and generalization capabilities. The research aims to enable intelligent systems in satellites, robots, and medical devices to adapt and improve their expertise in real-time, even with limited communication and power. The goal is to develop AI systems applicable for far edge computing that can learn in runtime and handle unanticipated situations. Why it matters: This research could lead to more resilient and adaptable AI systems for critical applications in remote and resource-constrained environments, with potential benefits for various sectors in the Middle East.
Dr. Maxim Panov from TII Abu Dhabi will give a talk on uncertainty estimation in neural networks, covering model calibration, ensemble methods, and Bayesian approaches. The talk will focus on efficient single-network methods for quantifying prediction confidence, without requiring ensembles or major training changes. Panov's background includes experience at Skolkovo Institute of Science and Technology and DATADVANCE Company. Why it matters: Improving uncertainty estimation is crucial for deploying reliable AI systems in critical applications across the GCC region.
MBZUAI Professor Fakhri Karray delivered a talk on advances in operational AI, highlighting its potential to grow global GDP by 15% by 2025. He discussed AI's impact on IoT, self-driving machines, virtual assistants, and other fields. Karray outlined milestones in AI, achievements in operational AI, future directions, and challenges for safe and beneficial AI. Why it matters: The presentation underscores MBZUAI's role in shaping the discourse around AI's transformative potential and ethical considerations in the region.
Dr. Mohammad Yaqub from MBZUAI will present AI solutions used to combat the COVID-19 pandemic, addressing healthcare consequences, social, economic, and policy-making decisions. The talk will cover the applications of AI and also discuss challenges like privacy, data needs, generalizability, data noise, and human acceptance. Yaqub's background includes a DPhil from the University of Oxford in Biomedical Engineering and research at the Institute of Biomedical Engineering, focusing on machine learning solutions for medical problems. Why it matters: This talk highlights the important role of AI in addressing pandemics and the ethical considerations that come with its application in healthcare and policymaking.
MBZUAI President Professor Eric Xing discussed AI's potential to augment human capabilities and the responsibility of AI researchers in shaping future leaders. Xing's background includes professorships at Carnegie Mellon University, leadership at Petuum Inc., and directorship of the Center for Machine Learning and Health. He also held visiting positions at Stanford University and Facebook Inc. Why it matters: The emphasis on responsible AI development and education aligns with the UAE's broader strategy to become a leader in ethical and human-centric AI.
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.