DumPy: NumPy except it's OK if you're dum

Summary

What I want from an array language is: Don’t make me think. Run fast on GPUs. Really, do not make me think. Do not. I say NumPy misses on three of these. So I’d like to propose a “fix” that—I claim—eliminates 90% of unnecessary thinking, with no loss of power. It would also fix all the things based on NumPy, for example every machine learning library. I know that sounds grandiose. Quite possibly you’re thinking that good-old dynomight has finally lost it. So I warn you now: My solution is utterly non-clever. If anything is clever here, it’s my single-minded rejection of cleverness. To motivate the fix, let me give my story for how NumPy went wrong. It started as a nice little library for array operations and linear algebra. When everything has two or fewer dimensions, it’s great. But at some point, someone showed up with some higher-dimensional arrays. If loops were fast in Python, NumPy would have said, “Hello person with ≥3 dimensions, please call my ≤2 dimensional functions in a loop so I can stay nice and simple, xox, NumPy.” But since loops are slow, NumPy instead took all the complexity that would usually be addressed with loops and pushed it down into individual functions. I think this was a disaster, because every time you see some function call like np.func(A,B), you have to think: OK, what shapes do all those arrays have? And what does np.func do when it sees those shapes? Different functions have different rules. Sometimes they’re bewildering. This means constantly thinking and constantly moving dimensions around to appease the whims of particular functions. It’s the functions that should be appeasing your whims! Even simple-looking things like A*B or A[B,C] do quite different things depending on the starting shapes. And those starting shapes are often themselves the output of previous functions, so the complexity spirals. Worst of all, if you write a new ≤2 dimensional function, then high-dimensional arrays are your problem. You need to decide what rules...