Isabelle/HOL as a language is nice, but the tooling has deep flaws even outside the pure desktop-first app approach.
The language is different (not necessarily better) in comparison to Lean, but I do agree with some of the points on dependent types. It seems both languages mostly just made different tradeoffs, which imo, were fair and have shaped them into quite efficient tools for their domains. The domain of "proofs" is large and different paradigms just have different strengths/weaknesses, Lean just specialized for a different part of this space.
Sledgehammer is nice but probably just a question of time until an equivalent can be ported/created for Lean. It might also be nice to use for explorative phases but is a resource hog, it also makes proofs concise but I would usually rather see the full chain of steps directly for a proof in the published code instead of a semi-magic "by sledgehammer".
Working on Isabelle itself however is painful (especially communicating with developers) in comparison to Lean. Things like "we don't have bugs just unexpected behaviour" on the mailing list just seems childish/unprofessional. The callout to RAM consumption of Lean and related systems is also a bit of joke when looking at Isabelle's gluttony for RAM.
For the HN crowd who are generally programmers but not necessarily mathematicians, it’s more relevant to consider the programming side of things. There is a very good book (one I haven’t finished unfortunately) that covers Lean from a functional programming perspective rather than proving mathematics perspective: https://leanprover.github.io/functional_programming_in_lean/
I am learning Lean myself so forgive me as I have an overly rosy picture of it as a beginner. My current goal is to write and prove the kind of code normal programmers would write, such as in the recent lean-zip example: https://github.com/kiranandcode/lean-zip/blob/master/Zip/Nat...
I recall an experiment in proving software correct from the 1990s that found more errors in the final proof annotations than in the software it had proved correct.
Then, I foresee 2 other obstacles, 1 of which may disappear:
1. Actually knowing what the software is supposed to do is hard. Understanding what the users actually want to do and what the customers are paying to do --which aren't necessarily the same thing--is complex
2. The quality of the work of many software developers is abysmal and I don't know why they would be better at a truth language than they are at Java or some other language.
Objection 2 may disappear to be replaced with AI systems with the attention to do what needs to be done. So perhaps things will change in that to make it worthwhile...
Re 1: Discussing and guiding the desirable theorems for general-purpose programs has been a major challenge for us. Proofs for their own sake (bad?) vs glorious general results (good but hard?). Actual human guidance there can be critical there at least for now.
For every "well of course, just...X, that's what everybody does" group-think argument there's a cogent case to be made for at least considering the alternatives. Even if you ultimately reject the alternatives and go with the crowd, you will be better off knowing the landscape.
Every time you go off the beaten path, you're locking yourself into less documentation, more bugs (since there's less exploration of the dark corners), and fewer people you can go to for help. If you've got 20+ choices to make, picking the standard option is the right choice on average, so you can just do it and move on. You have finite attention, so doing a research report on every dependency means you're never actually working on the core problem.
The exceptions to this are when a) it becomes apparent that the standard tool doesn't actually fit your use case, or b) the standard tool significantly overlaps the core problem you're trying to solve.
I think what's interesting about Lean is that Lean is a language, and what most people are talking about is a library called Mathlib. From what I've read about Mathlib, the creators are very pragmatic, which is why they encode classical logic in Lean types, with only a bit of intuitionistic logic[1].
[1] for those unfamiliar with math lingo, classical logic has a lot of powerful features. One of those is the law of the excluded middle, which says something can't be true and false at the same time. That means not not true is true, which you can't say in intuitionistic logic. Another feature is proof by contradiction, where you can prove something by showing that the alternative is unsound. There's quite a few results that depend on these techniques, so trying to do everything in intuitionistic logic has run into a lot of roadblocks.
> I think what's interesting about Lean is that Lean is a language, and what most people are talking about is a library called Mathlib. From what I've read about Mathlib, the creators are very pragmatic, which is why they encode classical logic in Lean types, with only a bit of intuitionistic logic
The computer science folks are now working on their own CSLib. https://www.cslib.iohttps://www.github.com/leanprover/cslib Given that intuitionistic logic is really only relevant to computational content (the whole point of it is to be able to turn a mathematical argument into a construction that could in some sense be computed with), it will be interesting to see how they deal with the issue. Note that if you write algorithms in Lean, you are already limited to some kind of construction, and perhaps that's all the logic you need for that purpose.
In constructive logic, a proof of "A or B" consists of a pair (T,P). If T equals 0, then P proves A. If T equals 1, then P proves B. This directly corresponds to tagged union data types in programming. A "Float or Int" consists of a pair (Tag, Union). If Tag equals 0, then Union stores a Float. If Tag equals 1, then Union stores an Int.
In classical logic, a proof of "A or not A" requires nothing, a proof out of thin air.
Obviously, we want to stick with useful data structures, so we use constructive logic for programming.
Classical logic has plenty of limitations/roadblocks, all logics do. Logic isn't a unified domain, but an infinite beach of structural shards, each providing a unique lens of study.
Classical logic was rejected in computer science because the non-constructive nature made it inappropriate for an ostensibly constructive domain. Theoretical mathematics has plenty of uses to prove existences and then do nothing with the relevant object. A computer, generally, is more interested in performing operations over objects, which requires more than proving the object exists.
Additionally, while you can implement evaluation of classical logic with a machine, it's extremely unwieldy and inefficient, and allows for a level of non-rigor that proves to be a massive footgun.
Classical logic isn’t rejected in computer science. Computer science papers don’t generally care if their proofs are non-constructive, just like in mathematics.
But proving the object exists is still useful, of course: it effectively means you can assume an oracle that constructs this object without hitting any contradiction. Talking about oracles is useful in turn since it's a very general way of talking about side-conditions that might make something easier to construct.
As far as I understand it, classical mathematics is non-constructive. This means there are quite a few proofs that show that some value exists, but not what that value is. And in mathematics, a proof often depends on the existence of some value (you can't do an operation on nothing).
The thing is it can be quite useful to always know what a value is, and there's some cool things you can do when you know how to get a value (such as create an algorithm to get said value). I'm still learning this stuff myself, but inuitionistic logic gets you a lot of interesting properties.
> As far as I understand it, classical mathematics is non-constructive.
It's not as simple as that. Classical mathematics can talk about whether some property is computationally decidable (possibly with further tweaks, e.g. modulo some oracle, or with complexity constraints) or whether some object is computable (see above), express decision/construction procedures etc.; it's just incredibly clunky to do so, and it may be worthwhile to introduce foundations that make it natural to talk about these things.
Would it be fair to say then that classical mathematics does not require computability, so it requires a lot more bookkeeping, while intuitionistic logic requires constructivism, so it's the air you live and breathe in, which is much more natural?
Intuitionistic logic is not really constrained to talking about constructive things: you just stuff everything else in the negative fragment. Does that ultimately make sense? Maybe, maybe not. Perhaps that goes too far in obscuring the inherent duality of classical logic, which is still very useful.
Ah, so if you had ¬p, and you negated it, you could technically construct ¬¬p in intuitionist logic, but only in classical logic could you reduce that to p? Since truth in classical logic means what you said here, where you didn't actually construct what p is, so you can't reduce it in intuitionistic logic.
> One of those is the law of the excluded middle, which says something can't be true and false at the same time.
That would be the law of non-contradiction (LNC). The law of the excluded middle (LEM) says that for every proposition it is true or its negation is true.
LEM: For all p, p or not p.
LNC: For all p, not (p and not p).
Classical logic satisfies both, intuitionistic logic only satisfies LNC.
> Another feature is proof by contradiction, where you can prove something by showing that the alternative is unsound.
As far as lean is concerned, this isn't an example of classical logic. It's just the definition of "not" - to say that some proposition implies a contradiction, and to say that that proposition is untrue, are the same statement.
Most "something"s that you'd want to prove this way will require a step from classical logic, but not all of them. (¬p ⟶ F) ⟶ p is classical; (p ⟶ F) ⟶ ¬p is constructive.
More generally, any negative statements can be proven classically, even in intuitionistic logic. Intuitionistic logic does not have the De Morgan duality found in classical logic, you have to go to linear logic if you want to recover that while keeping constructivity. (The "negative" in linear logic actually models requesting some object, which is dual to constructing it. The connection with the usual meaning of "negative" in logic involves a similar duality between "proposing" a proof and "challenging" it.)
So proof by contradiction proves ¬p, but it requires the law of excluded middle to prove p (in the case of ¬p -> F)? I didn't realize that was constructive in the first case.
The set theorists decided that mathematics is the overarching superdomain over all study of structure. You don't get to pick and choose. Either mathematics is a suburb of logic and these two things are separate, or they're not and ZFC dogmatics need to accept they don't have a monopoly on math.
I of course fully support reinstating logicism, but the same dogmatics love putting up a fight over that as well.
Not even the most dogmatic of the set theorists ever argued mathematics was possible without reason, however. For mathematics, logic is the world, as the copula makes no distinction between substance and existence. In the same sense that the earth is not matter itself, but it is a material thing.
Putting that aside, to make things more clear: computer science is mathematics. Computer scientists are mathematicians. That was a categorization decided long before you and I ever lived. In the sense that you mean, you're only referring to a very small fraction of what "mathematics" refers to In the true sense of the word. It is just as irreconcilably disjointed as Logic is, not unified and fundamentally non-unifiable.
I too think it would be better if "mathematics" was reserved for the gated suburb of ZFC. But that's not the world we live in, courtesy of the same people who pushed ZFC as a foundation to begin with.
Terence Tao, one of the most important living mathematicians, specifically embraces Lean and has been helping the community embrace it.
What you've done here is an overgeneralization. "People who like math" and "people who like computers" are massive demographics with considerable overlap.
The link is exactly what I’m saying. I only hear cs people talk about it.
For mathematicians a proof is a means to an end, or a medium of expression - they care about what they say and why.
The correspondence isn’t about C programs corresponding to proofs in math papers. It’s a very a specific claim about kinds of formal systems which don’t resemble how math or programming is done.
Isabelle/HOL as a language is nice, but the tooling has deep flaws even outside the pure desktop-first app approach.
The language is different (not necessarily better) in comparison to Lean, but I do agree with some of the points on dependent types. It seems both languages mostly just made different tradeoffs, which imo, were fair and have shaped them into quite efficient tools for their domains. The domain of "proofs" is large and different paradigms just have different strengths/weaknesses, Lean just specialized for a different part of this space.
Sledgehammer is nice but probably just a question of time until an equivalent can be ported/created for Lean. It might also be nice to use for explorative phases but is a resource hog, it also makes proofs concise but I would usually rather see the full chain of steps directly for a proof in the published code instead of a semi-magic "by sledgehammer".
Working on Isabelle itself however is painful (especially communicating with developers) in comparison to Lean. Things like "we don't have bugs just unexpected behaviour" on the mailing list just seems childish/unprofessional. The callout to RAM consumption of Lean and related systems is also a bit of joke when looking at Isabelle's gluttony for RAM.
For the HN crowd who are generally programmers but not necessarily mathematicians, it’s more relevant to consider the programming side of things. There is a very good book (one I haven’t finished unfortunately) that covers Lean from a functional programming perspective rather than proving mathematics perspective: https://leanprover.github.io/functional_programming_in_lean/
I am learning Lean myself so forgive me as I have an overly rosy picture of it as a beginner. My current goal is to write and prove the kind of code normal programmers would write, such as in the recent lean-zip example: https://github.com/kiranandcode/lean-zip/blob/master/Zip/Nat...
I recall an experiment in proving software correct from the 1990s that found more errors in the final proof annotations than in the software it had proved correct.
Then, I foresee 2 other obstacles, 1 of which may disappear:
1. Actually knowing what the software is supposed to do is hard. Understanding what the users actually want to do and what the customers are paying to do --which aren't necessarily the same thing--is complex
2. The quality of the work of many software developers is abysmal and I don't know why they would be better at a truth language than they are at Java or some other language.
Objection 2 may disappear to be replaced with AI systems with the attention to do what needs to be done. So perhaps things will change in that to make it worthwhile...
Re 1: Discussing and guiding the desirable theorems for general-purpose programs has been a major challenge for us. Proofs for their own sake (bad?) vs glorious general results (good but hard?). Actual human guidance there can be critical there at least for now.
Mind linking the experiment? Sounds interesting.
We need more of this.
For every "well of course, just...X, that's what everybody does" group-think argument there's a cogent case to be made for at least considering the alternatives. Even if you ultimately reject the alternatives and go with the crowd, you will be better off knowing the landscape.
It depends!
Every time you go off the beaten path, you're locking yourself into less documentation, more bugs (since there's less exploration of the dark corners), and fewer people you can go to for help. If you've got 20+ choices to make, picking the standard option is the right choice on average, so you can just do it and move on. You have finite attention, so doing a research report on every dependency means you're never actually working on the core problem.
The exceptions to this are when a) it becomes apparent that the standard tool doesn't actually fit your use case, or b) the standard tool significantly overlaps the core problem you're trying to solve.
I think what's interesting about Lean is that Lean is a language, and what most people are talking about is a library called Mathlib. From what I've read about Mathlib, the creators are very pragmatic, which is why they encode classical logic in Lean types, with only a bit of intuitionistic logic[1].
[1] for those unfamiliar with math lingo, classical logic has a lot of powerful features. One of those is the law of the excluded middle, which says something can't be true and false at the same time. That means not not true is true, which you can't say in intuitionistic logic. Another feature is proof by contradiction, where you can prove something by showing that the alternative is unsound. There's quite a few results that depend on these techniques, so trying to do everything in intuitionistic logic has run into a lot of roadblocks.
> I think what's interesting about Lean is that Lean is a language, and what most people are talking about is a library called Mathlib. From what I've read about Mathlib, the creators are very pragmatic, which is why they encode classical logic in Lean types, with only a bit of intuitionistic logic
The computer science folks are now working on their own CSLib. https://www.cslib.io https://www.github.com/leanprover/cslib Given that intuitionistic logic is really only relevant to computational content (the whole point of it is to be able to turn a mathematical argument into a construction that could in some sense be computed with), it will be interesting to see how they deal with the issue. Note that if you write algorithms in Lean, you are already limited to some kind of construction, and perhaps that's all the logic you need for that purpose.
When/why would one prefer to use intuitive logic, given the limitations/roadblocks?
In constructive logic, a proof of "A or B" consists of a pair (T,P). If T equals 0, then P proves A. If T equals 1, then P proves B. This directly corresponds to tagged union data types in programming. A "Float or Int" consists of a pair (Tag, Union). If Tag equals 0, then Union stores a Float. If Tag equals 1, then Union stores an Int.
In classical logic, a proof of "A or not A" requires nothing, a proof out of thin air.
Obviously, we want to stick with useful data structures, so we use constructive logic for programming.
You aren’t giving any justification why proofs should necessarily map to data structures.
Not necessarily, I only argue for utility. You can find better justification in the Curry-Howard correspondence.
Classical logic has plenty of limitations/roadblocks, all logics do. Logic isn't a unified domain, but an infinite beach of structural shards, each providing a unique lens of study.
Classical logic was rejected in computer science because the non-constructive nature made it inappropriate for an ostensibly constructive domain. Theoretical mathematics has plenty of uses to prove existences and then do nothing with the relevant object. A computer, generally, is more interested in performing operations over objects, which requires more than proving the object exists. Additionally, while you can implement evaluation of classical logic with a machine, it's extremely unwieldy and inefficient, and allows for a level of non-rigor that proves to be a massive footgun.
Classical logic isn’t rejected in computer science. Computer science papers don’t generally care if their proofs are non-constructive, just like in mathematics.
They care quite a bit actually, they just call their constructive proofs "algorithms" or "decision procedures".
But proving the object exists is still useful, of course: it effectively means you can assume an oracle that constructs this object without hitting any contradiction. Talking about oracles is useful in turn since it's a very general way of talking about side-conditions that might make something easier to construct.
As far as I understand it, classical mathematics is non-constructive. This means there are quite a few proofs that show that some value exists, but not what that value is. And in mathematics, a proof often depends on the existence of some value (you can't do an operation on nothing).
The thing is it can be quite useful to always know what a value is, and there's some cool things you can do when you know how to get a value (such as create an algorithm to get said value). I'm still learning this stuff myself, but inuitionistic logic gets you a lot of interesting properties.
> As far as I understand it, classical mathematics is non-constructive.
It's not as simple as that. Classical mathematics can talk about whether some property is computationally decidable (possibly with further tweaks, e.g. modulo some oracle, or with complexity constraints) or whether some object is computable (see above), express decision/construction procedures etc.; it's just incredibly clunky to do so, and it may be worthwhile to introduce foundations that make it natural to talk about these things.
Would it be fair to say then that classical mathematics does not require computability, so it requires a lot more bookkeeping, while intuitionistic logic requires constructivism, so it's the air you live and breathe in, which is much more natural?
Intuitionistic logic is not really constrained to talking about constructive things: you just stuff everything else in the negative fragment. Does that ultimately make sense? Maybe, maybe not. Perhaps that goes too far in obscuring the inherent duality of classical logic, which is still very useful.
Excluded-middle `true` means "[provable] OR [impossible to disprove]".
Intuitionist/Constructivist `true` means, "provable".
The question you are asking determines what answers are acceptable.
Why build an airplane, if you already know it must be possible?
Ah, so if you had ¬p, and you negated it, you could technically construct ¬¬p in intuitionist logic, but only in classical logic could you reduce that to p? Since truth in classical logic means what you said here, where you didn't actually construct what p is, so you can't reduce it in intuitionistic logic.
For ideological reasons.
You mean intuitionistic logic, not "intuitive logic".
Oops, just edited. I'm still fairly new to this area, so I keep mixing up my terms :)
> One of those is the law of the excluded middle, which says something can't be true and false at the same time.
That would be the law of non-contradiction (LNC). The law of the excluded middle (LEM) says that for every proposition it is true or its negation is true.
LEM: For all p, p or not p.
LNC: For all p, not (p and not p).
Classical logic satisfies both, intuitionistic logic only satisfies LNC.
This makes it good for formal maths, but bad for philosophy, since it means it can’t encode the speculative movement
> Another feature is proof by contradiction, where you can prove something by showing that the alternative is unsound.
As far as lean is concerned, this isn't an example of classical logic. It's just the definition of "not" - to say that some proposition implies a contradiction, and to say that that proposition is untrue, are the same statement.
Most "something"s that you'd want to prove this way will require a step from classical logic, but not all of them. (¬p ⟶ F) ⟶ p is classical; (p ⟶ F) ⟶ ¬p is constructive.
More generally, any negative statements can be proven classically, even in intuitionistic logic. Intuitionistic logic does not have the De Morgan duality found in classical logic, you have to go to linear logic if you want to recover that while keeping constructivity. (The "negative" in linear logic actually models requesting some object, which is dual to constructing it. The connection with the usual meaning of "negative" in logic involves a similar duality between "proposing" a proof and "challenging" it.)
So proof by contradiction proves ¬p, but it requires the law of excluded middle to prove p (in the case of ¬p -> F)? I didn't realize that was constructive in the first case.
Slightly off topic: This project https://agentcourt.ai/arb/analysis/index.html uses a Go/Lean hybrid design. The Go code is mostly glue, and the Lean code is the logic https://github.com/agentcourt/adjudication/tree/main/arb/eng.... It's not math-intensive. Really just functional programming with some interesting proofs (including soundness ideally). Go code can migrate to Lean code when that makes sense.
Good post! +1
Type theory and lean is more interesting to people who like computers than to people who like math.
The set theorists decided that mathematics is the overarching superdomain over all study of structure. You don't get to pick and choose. Either mathematics is a suburb of logic and these two things are separate, or they're not and ZFC dogmatics need to accept they don't have a monopoly on math.
I of course fully support reinstating logicism, but the same dogmatics love putting up a fight over that as well.
Mathematicians use logic to talk about the mathematical world. But logic is not the world.
Not even the most dogmatic of the set theorists ever argued mathematics was possible without reason, however. For mathematics, logic is the world, as the copula makes no distinction between substance and existence. In the same sense that the earth is not matter itself, but it is a material thing.
Putting that aside, to make things more clear: computer science is mathematics. Computer scientists are mathematicians. That was a categorization decided long before you and I ever lived. In the sense that you mean, you're only referring to a very small fraction of what "mathematics" refers to In the true sense of the word. It is just as irreconcilably disjointed as Logic is, not unified and fundamentally non-unifiable.
I too think it would be better if "mathematics" was reserved for the gated suburb of ZFC. But that's not the world we live in, courtesy of the same people who pushed ZFC as a foundation to begin with.
> For mathematics, logic is the world, as the copula makes no distinction between substance and existence.
No. There are truths about the subject not captured in any single formal system. Which is why objects are studied form many perspectives.
> Computer scientists are mathematicians.
Some are and some aren’t.
Terence Tao, one of the most important living mathematicians, specifically embraces Lean and has been helping the community embrace it.
What you've done here is an overgeneralization. "People who like math" and "people who like computers" are massive demographics with considerable overlap.
> one of the most important living
Maybe. But more clearly one of the most popular online.
citation needed, Tao certainly is on record using Lean and that carries some weight.
also, https://en.wikipedia.org/wiki/Curry%E2%80%93Howard_correspon... i.e. there's no reason it should be as you say.
The link is exactly what I’m saying. I only hear cs people talk about it.
For mathematicians a proof is a means to an end, or a medium of expression - they care about what they say and why.
The correspondence isn’t about C programs corresponding to proofs in math papers. It’s a very a specific claim about kinds of formal systems which don’t resemble how math or programming is done.
Mathematicians care about interesting ideas, not whether their theorems are true :-)
They care about if it’s true. But the role of the formal proof is a kind of spell checker or static analysis after they have the idea.