The Transformative Impact of Generative AI: Strategies, Applications, and Innovations
Speaker: Zhu Han – Houston, TX, United StatesTopic(s): Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing
Abstract
This speech explores the transformative potential of Generative AI (GAI) across various domains. Firstly, we examine the use of Large Language Models (LLMs) in repeated games to develop practical strategies aligning with the folk theorem's equilibrium conditions, enhancing cooperative behavior through future payoff considerations. Secondly, we address low-light image enhancement in teleoperation using diffusion-based AI-generated content (AIGC) models. A Vision Language Model (VLM)-empowered contract theory framework optimizes AIGC task allocation and pricing under information asymmetry, improving resource management for teleoperators and edge servers. In the realm of autonomous driving, we integrate Federated Learning (FL) with Vision-language models (VLMs) in Graph Visual Question Answering (GVQA), highlighting advancements in privacy preservation, reduced communication costs, and maintained model performance. Lastly, an LLM-based semantic communication (SC) framework for underwater communication is presented, demonstrating efficient data transmission and resilience against noise and signal loss by performing semantic compression and prioritization of image data. These innovations collectively illustrate the broad impact of AI technologies, shaping strategies, and enhancing applications across various fields.
About this Lecture
Number of Slides: 50Duration: 60 minutes
Languages Available: Chinese (Simplified), English
Last Updated:
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