Only few methods such as K nearest neighbors are non parametric, rest all methods such as SVM's, decision trees are all parametric.
So using it as a jargon is a bad idea.
(Note table near start of article, particularly entries for "large grant" and "nice place to have a meeting.")
and while I agree the term is really loaded, ML is different from statistics in its approach and the discoveries it made. For example, ML showed that some techniques are much more effective at scale (large datasets) than suspected by most statisticians, as recognized by "hard core" statisticians like Wasserman.
Traditionally statistics is more geared towards hypothesis testing and analytics. While Machine Learning is about making prediction.
The people that write the proposals are going to try to maximize their chances, think of it as marketing.
Nano-technology was terribly oversold so it lost it's glamor, but the concept is real, synthetic biology is real as a concept and only just now beginning to make some headway, eventually, long after it has lost it's glamor too it will be a mainstay of industry.
Just like the humble transistor is no longer glamorous, it's 'just' technology now. But in 1947 it was a miracle.
Buzzwords ideally should lose their power because the tech behind them becomes commonplace, when they're oversold we have a problem (looking at you, AI).