Yep, for instance see how recent results are advertised as showing that machine learning algorithms are "better than humans" in various tasks (image recognition, primarily).
The thing to keep in mind is that all that old tech did not "fail" as is often assumed. Expert systems worked perfectly fine, as did a host of other GOFAI techniques. It's all waiting for us just as the researchers left it when they jumped overboard to fields that still had funding, kind of like a Mary Celeste of science.
My hope is that rather than going through another AI winter we can find a way to pick it all up where the 80's and the 90's left it and advance the science further, absolutely so while keeping the best bits of the current machine learning explosion. Machine learning is not a new thing anyway- it too goes back in the early days of AI. We have more data and more computational power today, but there's practically no really new approaches - Deep learning, SVMs, Decision Trees... those are not really "new" as such, it's just their popularity that is a new thing.
So we're at a point where we need to learn our history, before we end up repeating it unwittingly. And we can- we have theoretical foundation, we have practical approaches, we can put the two together and win.