Logic based AI is definitely taking a backseat to data driven methods in the current environment but dead is a gross exaggeration. There are a large class of problems for which heuristic search in logic domains is the most performant technique, and a significant class of problems where SAT solvers are feasible solutions. Many of them are real world examples, rather than academic problems. Also, many techniques are evolved or emerging for systems of logic that can handle uncertainty. I've done work on hybrid systems combining rule-based systems with data-based systems (the typical process takes a rule-based system as a starting point and evolves it towards a pure data system as the data sets get large enough). However, starting with a rule-based system is actually a good approach for most start-ups when you don't have enough data to get performant models.