Those other approaches you mentioned don’t get much love because they don’t work. Their advocates worked on them for many years and have nothing to show for it.
In the same way that ANNs didn't work for quite some time, until we've had the compute and the data to train them successfully?
I get that it's important to prove that an idea is worthwhile, and the easiest way to do that is to use it to solve a practical problem. At the same time, I am conscious that we shouldn't put all our eggs in the deep learning basket: who knows where the ceiling is going to be.
Don't get me wrong, I like deep learning, and you have to be silly not to admit how successful it has been. But the field would be so much more boring if not for the people with alternative views and ideas.
Younger people don't realize there was strong bias against using neural networks in the late 90s up until Hinton's talk on NNs around 2007. I get the feeling we're going through the same thing where novel research is becoming ignored because everything must fit the deep learning paradigm to be noticed.
There are lots of ideas floating around in AI field. Some of them might be good, most are not. If you have an idea and want others to look at it you better demonstrate how it outperforms every other method when applied to some task.