Trustworthy and Continually Adaptable Multimodal AI Systems

Abstract

As our world advances, AI systems must evolve beyond static models to become more adaptive and continuously improving. These systems should seamlessly integrate new knowledge and capabilities throughout their operational lifespan while ensuring safe and robust interactions in dynamic, multimodal environments. Achieving this vision requires addressing key challenges across multiple fields, with a particular focus on memory, integration, adaptation, and reliability in multimodal AI systems. In this talk, I will introduce these challenges and present approaches that enable AI to continuously evolve while maintaining robustness, reliability, and safety in response to the growing complexity of the real world and user demands. First, I will explore multiple essential aspects to develop multimodal models that continuously accumulate and integrate knowledge from new tasks, datasets, or modalities. I will then introduce approaches that promote long-term model evolution and generalization while effectively mitigating catastrophic forgetting. Second, I will present methods to enhance a model’s ability to acquire new capabilities and further refine its existing skills through the generation of targeted and controllable synthetic data. These techniques empower models to expand their knowledge beyond initial training, enabling more precise and efficient adaptation to complex and long-horizon challenges. Finally, I will highlight the importance of ensuring model reliability and safety by enhancing reasoning, mitigating unintended biases, and preventing harmful outputs. These advancements are essential for developing trustworthiness AI systems that can evolve without compromising their integrity and effectiveness. I will conclude by outlining future directions for developing a scalable and trustworthy embodied continual learning agent and its applications in high-stakes real-world domains.

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