This is really only an argument that the example in the article is not realistic (which it doesn't have to be, it might be expository). There are in fact countless applications of machine learning in actual daily use, such as detecting credit card fraud, where simpler manual methods would perform measurably worse in terms of money lost.
Sure, there are realistic applications of Machine Learning with great results.
But the article has failed in its headline goal ("ML is easier than it looks") if it chooses an example that is mathematically more complicated and still less effective than a naive alternative.
The first difficult task is to identify an effective method.
