AI engineering taskforce releases new report

At a critical point in the advancement of artificial intelligence, Professor Eric Miller and fellow experts discuss how to leverage AI for the betterment of society.
Professor Eric Miller.

As artificial intelligence (AI) becomes increasingly prevalent and powerful, it has the potential to shape the future of engineering and other key areas. At Tufts University, faculty members are on the leading edge of artificial intelligence research. Professor Eric Miller of the Department of Electrical and Computer Engineering, director of the Tufts Institute for Artificial Intelligence, recently participated in a task force aimed at determining best practices for establishing AI engineering on a broad scale. Assembled by the Engineering Research Visioning Alliance (ERVA), Miller and 27 fellow AI experts from academia, industry, and government gathered to discuss the future role of AI in society.

The group focused on how AI engineering can best address the “14 grand challenges of engineering” set out by the National Academy of Engineering in 2008. With challenges ranging from cybersecurity to sustainability, the group considered how AI engineering could benefit each goal. The resulting report, “AI Engineering: A Strategic Framework to Benefit Society,” encourages collaboration among academia, government, and industry to harness the growing potential of AI.

The ERVA is a National Science Foundation-funded initiative that convenes, catalyzes, and enables the engineering community to identify nascent opportunities and priorities for engineering-led innovative, high-impact, cross-domain research that addresses national, global, and societal needs. This is the eighth report from the ERVA, and the initiative continues to determine important future engineering research directions.

Miller joined Tufts in 2007 and has secondary appointments in the Department of Computer Science, the Department of Biomedical Engineering, and the Department of Mathematics. His research interests include physics-based tomographic image formation and object characterization, inverse problems, regularization, statistical signal and imaging processing, and computational physical modeling. His work has implications across a range of contexts including medical imaging, nondestructive evaluation, environmental monitoring and remediation, landmine and unexploded ordnance remediation, and automatic target detection and classification.

The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Science Foundation. Research reported in this article was supported by the National Science Foundation under the following award number: 2048419