How can "any process you can imagine" be thought of as function computation? I stumbled upon this passage when reading this guide.

Universality theorems are a commonplace in computer science, so much
so that we sometimes forget how astonishing they are. But it's worth
reminding ourselves: the ability to compute an arbitrary function is
truly remarkable. Almost any process you can imagine can be thought of
as function computation.* Consider the problem of naming a piece of
music based on a short sample of the piece. That can be thought of as
computing a function. Or consider the problem of translating a Chinese
text into English. Again, that can be thought of as computing a
function.  Or consider the problem of taking an mp4 movie file and
generating a description of the plot of the movie, and a discussion of
the quality of the acting. Again, that can be thought of as a kind of
function computation.* Universality means that, in principle, neural
networks can do all these things and many more.

How is this true? How can any process be thought of as function computation? How would one compute function in order to translate Chinese text to English?
A function is simply a procedure that maps a particular input to a particular output. You put in $X$, and the function computes $Y$. Those $X$ and $Y$ can take many different forms. It could be mapping one number to another number (convert miles to kilometres), mapping sound to text (name that tune), mapping text to text (translate languages), mapping a video to text (review this movie), or mapping text to an image (draw a picture of $X$). Anytime you have a procedure that produces a fixed output based on a fixed input, it's a function.
Universality theorems guarantee that a neural network can produce an arbitrarily good approximation of any possible function. That doesn't mean it's easy, though - finding the right function that maps $X$ to $Y$ is the hard part.