> The programmer has to model what the inference system is doing in his or her head, and it's a source of significant cognitive load.
Huh? I don't know what your type-inferred language of choice is, but personally I never have to go down to the level of solving constraint sets and unifying types in my head when working any of the ML dialects. Even when writing my own inference algorithms, once I'm able to establish confluence and progress I rarely give the main inference constraint solver a second thought when writing code.
See my response to tikhonj. No, of course not, because you're naturally good at PL theory.
Or maybe artificially good at it. Whatever the reason, you're good at it. But I believe it's clear that most people, even most programmers, aren't naturally good at it. And a lot of effort have been invested in trying to make them artificially good at it, without much result as I can see.
The number of extremely smart people I've met, who nonetheless think Haskell is too smart for them, is considerable. This is quite simply a UI problem. If you have a UI problem and you either don't try to solve the problem, or do and fail, you get an adoption problem. Haskell has an adoption problem, n'est ce pas? So where does my reasoning go wrong?
> Haskell has an adoption problem, n'est ce pas? So where does my reasoning go wrong?
The problem is thinking that this comes from unification-based inference algorithms which is an implementation detail thats fairly far removed from the day to day programmer concerns. Most Haskell programmers are not type theorists and don't implement HMF algorithms. The only difference between a Haskell programmer and "most programmers" is that they've taken the time to learn the language. In my experience many people turn away from Haskell because they can't immediately transfer their existing knowledge into it and don't like feeling like a beginner again, so they stop. I've met many /more/ people who think that programming in Java is too smart for them as well, it's just a matter of putting the time in to learn.
My experience is that there are two very different kinds of Haskell programmers: those who really understand the math, like you, and those who treat it as a black box (the "Learn You A Haskell" contingent).
As a black-box language, I think Haskell is of nontrivial value, but the black box is very deep and weird. This is a UI problem. If you expect people to understand the theory, this is also a UI problem in a different way.
So, two UI problems add up to a big UI problem, which is my theory about the adoption issues. I'm curious as to what your theory is.
> If you expect people to understand the theory, this is also a UI problem in a different way.
I don't really understand what you mean by "theory", you don't need to know /any/ PL theory to write Haskell and you certainly don't need to know mathematics. What specific ideas are you referring to that require advanced knowledge as a prerequisite for doing day-in-day-out programming tasks in Haskell?
Again, you have to be able to perform the same computation as the type inference algorithm - for any inference engine, any type system, any language, you can't use it if you don't know what it's going to do.
There are a lot of different ways of handling this problem in Haskell - some involve knowing the notations and results of the branch of math called "PL theory," some don't.
As UIs, they all have drawbacks - and we know this, because you and I both know all the ways Haskell is awesome and rocks. We also know what people say when they complain about Haskell. They're complaining about exactly this material - so why not take the customer at his word?
> for any inference engine, any type system, any language, you can't use it if you don't know what it's going to do.
My whole argument is that you don't need to know anything about the implementation of the inferencer to use it, you don't need to predict it's behavior anymore than you need to model the CPU instruction selection of the compiler in your head. Most of the time you can safely program at a level of abstraction that doesn't involve the implementation of the language, and in Haskell this "normal level" is well above the level that involves low-level things like the lambda calculus.
I think we're just an impasse, the meme that you need mathematics or PL theory to program in Haskell is one that puzzles me and I don't understand where it comes from. Best of luck on your project.
I'm not sure it's realistic to say someone could make a career in a programming language without being able to look at a function and deduce the static type of a value without asking the computer.
As I wrote elsewhere in the comments:
I don't know HM too well, but it seems to have more in common with the dataflow analysis algorithms in optimizers and verifiers -- which programmers don't usually have to understand -- than the basic unidirectional type inference that already serves pretty well to reduce redundancy in the source code.
I could imagine citing C++ as a counterargument -- no one understands the type system, but people use the language anyway -- but it's still not an abstraction you don't have to understand to use, like a CPU.
> My experience is that there are two very different kinds of Haskell programmers: those who really understand the math, like you, and those who treat it as a black box (the "Learn You A Haskell" contingent).
I don't think that LYAHFGG treats Haskell as a black box, not for the level that it aims to teach Haskell - it makes it very clear that all functions are curried, emphasizes that a functor should be viewed more as of a "computational context" instead of a "box" (for example function composition), and so on. I've found it to be as precise and vigorous as other tutorials on Haskell, for the level it aims at.