Concurrent systems may execute one of an exponential number of paths resulting in a large number of potential states. This often makes it infeasible to consider all possible behaviors of a system. Scalable formal methods, while not guaranteeing correctness, can provide greater confidence about properties of a system. The talk will describe three such methods: Statistical Model Checking (SMC), Euclidean Model Checking (EMC), and Predictive Runtime Verification (PRV).
TechConnect brings students and employers together for networking, education and connections. Twice each year, employers connect with Engineering and Computer Science students in an open, dynamic networking environment. Students come prepared with resumes to meet industry and tech representatives and learn about employment opportunities available, the characteristics employers seek, and also a realistic and insightful view of the job market and career paths for students interested in engineering and technical careers.
I will present the 'virtual democracy' framework for the design of ethical AI. In a nutshell, the framework consists of three steps: first, collect preferences from voters on example dilemmas; second, learn models of their preferences, which generalize to any (previously unseen) dilemma; and third, at runtime, predict the voters' preferences on the current dilemma, and aggregate these virtual 'votes' using a voting rule to reach a decision.