Balancing Quality and Efficiency in Future AI Systems

Speaker:  Shiqiang Wang – Exeter, United Kingdom
Topic(s):  Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing

Abstract

Quality and efficiency are both essential in AI systems as data sources become more diverse and model sizes grow. In this talk, I will present techniques to address challenges in data and model quality as well as their efficiency, which are essential for building high-performing and sustainable AI systems. I will first introduce a toolkit for enhancing the quality of datasets, which can be used in a broad range of learning tasks including the training or fine tuning large language models (LLMs), laying the groundwork for model training with good data. Then, considering the specific challenge where data is distributed unevenly across sources with varying sizes, quality, and availability, such as in the case of federated learning, I will introduce the FedAU algorithm. This algorithm dynamically adjusts aggregation weights in the model training process based on the availability of data sources, to prevent model bias and improve training convergence. Afterwards, I will introduce techniques to make both training and inference more efficient, focusing on a framework that optimizes model selection from a zoo of LLMs to minimize energy usage while maintaining model performance guarantees. Together, these approaches form a blueprint for future AI systems that are capable of learning effectively from a vast amount of data at diverse sources and delivering high quality models while enhancing resource efficiency in real-world applications.

About this Lecture

Number of Slides:  30
Duration:  60 minutes
Languages Available:  English
Last Updated:  12/05/2026

Request this Lecture

To request this particular lecture, please complete this online form.

Request a Tour

To request a tour with this speaker, please complete this online form.

All requests will be sent to ACM headquarters for review.