Julia could be a great language for ML. It needs more mindshare and developer attention though
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Then again, I am also open to the fact that I'm jammed up by the production use of dynamically typed languages, and maybe the "for ML" part means "I code in Jupyter notebooks" and thus give no shits about whether person #2 can understand what's happening
explicit and strict types on arguments to functions is one way, but certainly not the only way, nor probably the best way to effect that
I readily admit that I am biased in that I believe that having a computer check that every reference to every relationship does what it promises, all the time
more generally, the most important bits of a particular function to understand is
* what should it be called with
* what should it return
* what side effects might it have
and the "what" here refers to properties in a general sense. types are a good shortcut to signify certain named collections of properties (e.g., the `Int` type has arithmetic properties). but there are other ways to express traits, preconditions, postconditions, etc. besides types
they sure can...
That being said, I've always found the argument that types can be overly restrictive and prevent otherwise valid code from running unconvincing. I've yet to see dynamic code that benefits from this alleged advantage.
Nearly universally the properly typed code for the same thing is better, more reliable and easier for new people to understand and modify. So sure, you can avoid all of this if the types are really what bother you, but it feels a bit like saying "there are stunts I can pull off if I'm not wearing a seatbelt that I just can't physically manage if I am."
If doing stunts is your thing, knock yourself out, but I'd rather wear a seatbelt and be more confident I'm going to get to my destination in one piece.