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I felt the article was quite fair in acknowledging the intriguing results of deep learning, and decidedly arguing against ignoring them. "Elegance" can indeed

I felt the article was quite fair in acknowledging the intriguing results of deep learning, and decidedly arguing against ignoring them. "Elegance" can indeed not be defined, but I believe the misunderstanding here is more about the point of view: Deep neural networks are plenty elegant when looked at from an ML POV. But, if you're an expert in, for example, image processing, using a DNN as a tool, the solution it may give you won't be "elegant" by anybody's definition. It is, after all, a repetitive formula with X million arbitrary floats. Previous ML methods usually resulted in models, or formulas, that were small enough to "grasp" intuitively. The "beauty", or "elegance" was that often, you could find connections from terms in your model to the real world.

ganfortran 10y ago
> Previous ML methods usually resulted in models, or formulas, that were small enough to "grasp" intuitively. Which is not true in practice. SVM in reality is an instance based method, with thousands to 10 thousands high-dimensional vectors as its 'parameters'. Random forests, which is famous for its ease of interpretability is no easy meal either when you end up with 100s of trees with outrageous branches. Not mention that real world model are embarrassingly complex ensemble of smaller but still complex models.