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.
A new survey paper provides a deep dive into post-training methodologies for Large Language Models (LLMs), analyzing their role in refining LLMs beyond pretraining. It addresses key challenges such as catastrophic forgetting, reward hacking, and inference-time trade-offs, and highlights emerging directions in model alignment, scalable adaptation, and inference-time reasoning. The paper also provides a public repository to continually track developments in this fast-evolving field.
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 has launched the Institute of Foundation Models to advance generative AI research and development. The institute will focus on developing large-scale AI models adaptable to various applications, building on MBZUAI's existing work with models like Jais, Llama 2, and Vicuna. It will focus on multi-modal foundation models applicable to areas like healthcare, finance and environmental engineering. Why it matters: This initiative further solidifies the UAE's position as a leader in AI, particularly in the development and application of foundation models for diverse industries.
Dr. Jindong Wang from Microsoft Research Asia gave a talk at MBZUAI about the limitations of large foundation models, including adapting to real-world unpredictability and security concerns. He also discussed the need for interdisciplinary collaboration to evaluate the benefits and risks of these models. Dr. Wang shared his research and insights on how to harness the power of large foundation models while addressing their constraints and fostering responsible AI integration. Why it matters: This highlights MBZUAI's role in hosting discussions about responsible AI development and the challenges of deploying foundation models.
This article discusses the increasing concerns about the interpretability of large deep learning models. It highlights a talk by Danish Pruthi, an Assistant Professor at the Indian Institute of Science (IISc), Bangalore, who presented a framework to quantify the value of explanations and the need for holistic model evaluation. Pruthi's talk touched on geographically representative artifacts from text-to-image models and how well conversational LLMs challenge false assumptions. Why it matters: Addressing interpretability and evaluation is crucial for building trustworthy and reliable AI systems, particularly in sensitive applications within the Middle East and globally.
TII and LightOn have partnered to build the NOOR Platform for exascale computing, aimed at developing foundation models. The collaboration will leverage LightOn's expertise in large language models, with the first output being the largest Arabic language model to date. The platform will provide high-quality data pipelines and facilitate extreme-scale distributed training and serving. Why it matters: This partnership aims to establish Abu Dhabi as a center of AI excellence and boost the UAE's ambitions in high-tech innovation and NLP research.
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.