Perspectives on Quantum Computing: Education, Applications, and the Future of the Field

SEI Quantum Computing team members Dr. Jason Larkin, Daniel Justice, and Matias Jonsson discuss what Jonsson is learning as a student intern in quantum computing, applications where quantum computing can offer advantages over classical computers, and where they think the quantum field is going.

In these short videos, experts from the Software Engineering Institute (SEI) deliver informative snapshots of our latest research on the changing world of all things cyber. The SEI is a federally funded research and development center sponsored by the U.S. Department of Defense and operated by Carnegie Mellon University.

CMU's Alex Davis' take on AI

Alex Davis is an Assistant Professor in the Department of Engineering and Public Policy at Carnegie Mellon University. He is a member of the Behavior, Decision, and Policy Group, the Carnegie Electricity Industry Center, and the Center for Climate and Energy Decision Making. He is currently CMU's acting director of a multi-year, multi-institutional research project on the relationship between science and proven experience. His research focuses on the behavioral foundations of policy, applied to innovation and entrepreneurship, energy, the environment, health, and information and communication technologies.

Each webinar features an SEI researcher discussing their research on software and cybersecurity problems of considerable complexity. The webinar series is a way for the SEI to accomplish its core purpose of improving the state-of-the-art in software engineering and cybersecurity and transitioning this work to the community. The SEI is a federally funded research and development center sponsored by the U.S. Department of Defense and operated by Carnegie Mellon University. The SEI Webinar Series is produced by SEI Communications Outreach.

Measuring the Trustworthiness of AI Systems

The ability of artificial intelligence (AI) to partner with the software engineer, doctor, or warfighter depends on whether these end users trust the AI system to partner effectively with them and deliver the outcome promised. To build appropriate levels of trust, expectations must be managed for what AI can realistically deliver. In this podcast from the SEI’s AI Division, Carol Smith, a senior research scientist specializing in human-machine interaction, joins design researchers Katherine-Marie Robinson and Alex Steiner, to discuss how to measure the trustworthiness of an AI system as well as questions that organizations should ask before determining if it wants to employ a new AI technology.