The "gotcha" is that Haskell is heavy duty machinery, and getting up to speed with it if your background lacks solid type-theory can be really daunting. For that reason alone, it could never be the default. Sometimes I like to think about how much of a disservice academia has done to itself by training mathematicians without giving them the foundational knowledge they need to utilize the nuclear-grade tooling they themselves have the most potential to benefit from. For a number theorist? Sure makes sense. But the fact that machine learning courses don't have rigorous undergraduate prerequisites in learning the foundation of computation is pure absurdity.

[1] - http://hasktorch.org/

[ggml]https://github.com/ggml-org/ggml

The worst I have had in a while is when I was dealing with a sparse array data type and getting some processing done in a C function to speed things up.

Infuriatingly enough, the index type used to change after initialising the array. It would check if Int32 could accomodate the size of the array. If it could it would downgrade to Int32 at runtime. My C counterpart had no clue.

That was so not fun to debug because the errors were intermittent (depending on the fill factor of the sparse array)

An appropriate tensor can certainly be represented by a multidimensional array but not all multidimensional arrays are tensors. It depends on how that multidimensional array participates with others.

For example, (lat, long) is not a 2d vector although it can be represented as a 2 dimensional array.