[iujjkfjdkkdkf]

But will taking away abstractions address what the paper is talking about?

If we can abstract away the models and training, I see an opportunity to focus more of the dataset. If you look at something like Pytorch Lightning (and I think fast.ai but am not familiar) you can deploy very powerful, and proven, models without having to get into the details. What might still be missing are more tools to work with the dataset - easily varying size, composition, using active learning, etc. But it's all supported by more abstraction.

(I'm a huge believer in the importance of understanding things from first principles, so I agree with making sure students develop their skills that way. But when it comes to practical work with datasets, abstraction is very important)

[avs733]

At a professional level not directly, but I believe (opinion statement) starting with the abstractions and the abstractions being the focus of all the coursework you typically take contributes to the problem over the long haul.