Resource-Efficient and Agentic AI Systems

Professor: Haojin Yang

Resource-Efficient and Agentic AI Systems aim to develop AI models that are not only powerful but also environmentally sustainable and capable of autonomous decision-making. Key applications include creating energy-efficient AI algorithms, developing self-improving AI agents, and designing AI systems that can operate with limited computational resources. Advanced concepts in this field involve the use of neuromorphic computing for energy-efficient AI and the development of meta-learning techniques for rapid adaptation. The impact of this approach is crucial in creating AI systems that are both powerful and sustainable, addressing concerns about the environmental impact of AI. Future trends in this area include the development of AI systems that can optimize their own resource usage and the creation of distributed AI networks that can share computational loads efficiently.