CS Events

Qualifying Exam Advancing AI Sustainability
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Speaker: Zhuowei Li

Location : CoRE 305

Committee: 

Professor Dimitris N Metaxas (Chair)

 Assistant Professor Yongfeng Zhang

 Associate Professor Konstantinos Michmizos

Professor Badri Nath

Event Type: Qualifying Exam

Abstract: Recent advancements in AI have occasionally outperformed human capabilities in specific domains. Yet, these models often lag behind human adaptability due to their static nature, compartmentalized knowledge, and limited extensibility. This gap becomes evident as rapidly evolving environments and task requirements render even the most sophisticated models quickly outdated. In contrast, humans possess a dynamic learning capability that allows for continuous adaptation and relevance across their lifespan. Addressing this challenge, my presentation focuses on the development of more resilient and sustainable AI models that can integrate effectively in real-world applications, both at scale and within practical budget constraints. I will introduce two innovative approaches in this domain. Towards Self-Supervised and Weight-preserving Neural Architecture Search: This method facilitates label-free model design, eliminating the dependency on extensive labeled datasets and reducing the time and resources required for model training. Steering Prototypes for Rehearsal-free Continual Learning: This approach addresses the issue of model obsolescence by enabling AI systems to learn continually, adapt to new information, and update their knowledge base without the need for constant retraining or external guidance. These methodologies not only enhance the sustainability of AI models but also bring them a step closer to mirroring the human capacity for lifelong learning and adaptation. My talk will delve into the technical aspects of these approaches and discuss their potential implications for the future of AI development.

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Contact  Professor Dimitris Metaxas (Chair)