Deep learning based systems are at least trustable as working animals I think... They share a lot of similarities. You can not know how it thinks, but you can train them, observe them, and then trust them to a certain level.
I am quite interested in the difference between people's intuition about things. The more complex a system is, the less I tend to think about it being designed.. It is just too beautiful to be a crafted artifact.
This is rather a math-specific operation (type conversion on math operators), not the scope of general purpose language. A lot of dependent typed language can achieve this naturally and most languages with ADT can simulate this.
Math is a kind of a core use case for, really, almost any programming language, and is central to much of what computers are used for (as is hinted at in their name.)
Though dealing with this aspect of units is less “math-specific” than central to the relation between math and real-world meaning, which is arguably even more important than abstract math in computing applications.
A commercial project aims for profit, not public service. The previous post explains why they are popular demands. And it is natural for commercial project to satisfy popular demands.
In Python people are encouraged to follow styles for code as a communication tool between developers. If you just really want your code to "look nice" you can have things like this:
def begin(*args):
return args[-1]
begin(
func := lambda x, y: begin(
z := int(input()),
x + y + z
),
func(1, 2)
)
I was thinking of the exact same thing when writing my new project in Python, and implemented a poor-man's version using typing.NamedTuple. But my idea was mainly from the extensive records from Elm. Although Elm also have Maybe, but the idioms of "making impossible things unrepresentative" eliminates a lot of unwanted usage.
