IBM Fellow Dr. Tanveer Syeda-Mahmood gave a talk on the evolution of foundational models, covering multimodal fusion in healthcare and neuro-inspired AI for computer vision. She also discussed image-driven fact-checking of generative AI textual reports for responsible models. Dr. Syeda-Mahmood leads IBM's work in Multimodal Bioinspired AI and WatsonX features, and previously led the Medical Sieve Radiology Grand Challenge. Why it matters: The talk highlights the ongoing development and application of AI foundational models in critical areas like healthcare and responsible AI development, showing IBM's continued investment in these areas.
Paul Liang from CMU presented on machine learning foundations for multisensory AI, discussing a theoretical framework for modality interactions. The talk covered cross-modal attention and multimodal transformer architectures, and applications in mental health, pathology, and robotics. Liang's research aims to enable AI systems to integrate and learn from diverse real-world sensory modalities. Why it matters: This highlights the growing importance of multimodal AI research and its potential for advancements across various sectors in the region, including healthcare and robotics.
MBZUAI researchers Darya Taratynova and Shahad Hardan developed Forget-MI, a method for making clinical AI models "unlearn" specific patient data without retraining the entire model. Forget-MI addresses the challenge of removing patient data from AI models trained on multimodal records (like chest X-rays and reports) due to regulations like GDPR and HIPAA. The method unlearns both unimodal (image or text) and joint (image-text) associations while retaining overall accuracy using a late-fusion multimodal classifier. Why it matters: This research provides a practical solution to a critical privacy concern in healthcare AI, enabling compliance with data protection regulations and fostering trust in AI-driven medical applications.
The AI4Bio Workshop at MBZUAI explored the intersection of AI and biology, focusing on AI-driven virtual organisms and foundation models. Eric Xing presented his vision of using AI to simulate biological activities, offering a safer alternative to physical experiments. Researchers like Le Song and Jen Philippe Vert are developing foundation models for biological systems, enhancing drug discovery and bioengineering. Why it matters: This signals the growing importance of AI in advancing biological research and healthcare innovation within the UAE and globally.
Pietro Liò from the University of Cambridge will discuss geometric deep learning techniques for building a digital patient twin using graph and hypergraph representation learning. The talk will focus on integrating Computational Biology and Deep Learning, considering physiological, clinical, and molecular variables. He will also cover explainable methodologies for clinicians and protein design using diffusion models. Why it matters: This highlights the growing interest in applying advanced AI techniques like geometric deep learning and diffusion models to healthcare challenges in the region, particularly for personalized medicine.