Improving the Reliability of AI Systems from a Software Engineering Perspective

Artificial Intelligence techniques have been widely applied to solve real-world challenges, from autonomous driving cars, to detecting diseases. With the popularity of 5G wireless network, more and more AI systems are being developed to provide convenient services to everyone. It is important to ensure the reliability and quality of AI systems from every phase in software development cycle, i.e., development, integration, deployment and monitoring. In this collaboration with Ericsson GAIA, we will propose techniques to systematically improve the quality and reliability of AI systems. In particular, we will explore new testing techniques to test AI systems, especially the underlying AI models. Moreover, we will adapt and improve explainable AI models for the purpose of debugging and optimizing AI models. Automated techniques to support the development, debugging, deployment and monitoring of AI systems will enrich the ecosystem of AI systems in the era of 5G.

Faculty Supervisor:

Jinqiu Yang

Student:

Zi Peng;Triet Pham

Partner:

Ericsson Canada

Discipline:

Engineering - computer / electrical

Sector:

Information and cultural industries

University:

Concordia University