It's not that the author is incorrect, it's just that the argument he used was a straw man. i.e. they are unrelated domains. However the metrics that are used t

It's not that the author is incorrect, it's just that the argument he used was a straw man. i.e. they are unrelated domains. However the metrics that are used to qualitatively measure the performance of each model's performance on their respective problem domain may be useful for research. For example, we know that deep neural nets are currently state-of-the-art for image recognition, and so if our problem involves a similar image recognition problem, we might be wise to start off with a neural net. We don't have to start from scratch; we can use "transfer learning" to get some base weights (bottleneck features) going and refine our model from there. The point is, there is no panacea, no "ultimate algorithm" for any and every problem, and yet the author demands this of machine learning in that section of his writing.