SVM algorithm is quite old - it was developed 1960s, but was extremely popular in 1990s and 2000s. It is a classical (and quite beautiful) part of machine learning courses.
Today it seems that in media processing (images, sound etc.) neural networks completely dominate, while in other areas Gradient Boosting has very strong positions.
Also, in recent data competitions I observe no SVM-based solutions.
I am looking for application examples where SVM still gives state-of-art results (as of 2016).
Update: I'd like to have some example which I can give e.g. to students / colleagues when explaining SVM so that it doesn't look like purely theoretical or deprecated approach.