Lean was a gamechanger for me as someone who has a "hobby" level interest in abstract mathematics. I don't have the formal education that would have cultivated the practice and repetition needed to just know on a gut level the kinds of formal manipulations needed for precise and accurate proofs. but lean (combined with its incredibly well designed abbreviation expansion) gives probably the most intuitive way to manipulate formal mathematical expressions that you could hope to achieve with a keyboard.
It provides tools for discovering relevant proofs, theorems, etc. Toying around with lean has actively taught me math that I didn't know before. The entire time it catches me any time I happen to fall into informal thinking and start making assumptions that aren't actually valid.
I don't know of any way to extract the abbreviation engine that lean plugins use in the relevant editors for use in other contexts, but man, I'd honestly love it if I could type \all or \ne to get access to all of the mathematical unicode characters trivially. Or even extend it to support other unicode characters that I might find useful to type.
Bessis [1] argues that formalism - or loosely math writing - is foundational to clarifying intuition/meaning in a way that natural language cannot. Imagine it as a scalpel carving out precise shapes from the blur of images we carry thereby allowing us to "see" things we otherwise cannot.
I am curious to try out lean to understand how definitions in lean are able to operationally capture meaning in an unambiguous manner.
For mathematics and certain fields, that is true. But the formalism matters, and as some have argued, the Fregean style that came to dominate in the 20th century is ill-suited for some fields, like linguistics. One argument is that linguists using this style inevitably recast natural language in the image of the formalism. (The traditional logical tradition is better suited, as its point of departure is the grammar of natural language itself.)
No formalism is ontologically neutral in the sense that there is always an implied ontology or range of possible ontologies. And it is always important to make a distinction between the abstractions proper to the formalism and the object of study. A common fallacy involves reifying those abstractions into objects of the theory, at least implicitly.
It is interesting that you argue for formalism using a metaphor in natural language, rather than use a mathematical/data oriented argument. I find the metaphor pleasing in a way that I suspect a more data driven argument would not be.
I mean, if you understand leans system then you understand the formal manipulation needed for precise and accurate proofs. Most mathematical papers are rather handwavy about things and expect people to fill in the formalism, which is not always true, as we have seen
I've been excited about Lean for years, not because of correctness guarantees, but because it opens the door to doing maths using software development methods.
Libraries of theorems and mathematical objects, with well defined abstractions that are ergonomic to apply in target use cases. Accompanied by good documentation, focused less on how the theorems are proven (how the functions are implemented), and more on what to use them for and how. With proper version control and package management.
I believe that all these practices could vastly improve collaboration and research velocity in maths, as much or more than AI, although they are highly complementary. If maths is coding, AI will be much better at it, and AI will be more applicable to it.
As a a hobbyist mathematician / type theorist, chatgpt et al are great at 'looking up' theorems that you want to exist but that you may not have read about yet. It's also good at connecting disparate areas of math. I don't think lean subsumes AI. Rather, lean allows you to check the AI proof. ChatGPT genuinely does have a knack for certain lines of thought.
LLMs and Lean are orthogonal, neither subsumes either.
They both can be useful or harmful, do to their respective strengths and trade offs.
PAC/statistical learning is good at needles in the haystack problems assuming that the tail losses, simplicity bias, and corpus representation issues are acceptable and you understand that it is fundamentally existential quantification and control for automation bias etc…
Lean is a wonderful collection of concepts and heuristics but due to Rice and Gödel etc… will not solve all problems with software development.
How Gödel’s second incompleteness theorem shows that you can prove anything, without that proof being meaningful is a lens into that.
It is horses for courses, and remember that even in sub-TC total functional programming, proving and arbitrary functions is very hard, while constructing one is far more tractable.
Even those proofs don’t demonstrate semantic correctness. History is riddled with examples of people using powerful tools that elegantly explain flawed beliefs.
The 2009 crash and gaussian copula as an example.
Get all the value you can out of these tools, but use caution, especially in math, where superficially similar similarities often have conflicting conventions, constraints, and assumptions.
Obviously if you problem is ergotic with the Markov property, both will help, but Automated theorem proving and PAC learning will never be a meta theory of the other IMHO.
Terence Tao is well known for being enthusiastic about Lean and AI and he regularly posts about his experiments.
He is also a serious research mathematician at the top of his game, considered by many one of the best mathematicians alive. This might be biased by the fact that he is such a good communicator, he is more visible than other similarly good mathematicians, but he is a Fields medallist all the same.
Kevin Buzzard has been the main mathematician involved with Lean
This is a recent talk where he discusses putting it together with LLMs (he's somewhat sceptical it'll be revolutionary for producing new mathematics any time soon)
I'm leaning a lot into AI + lean. It's a fantastic tool to find new proofs. The extremly rigid nature of lean means you can really check programs for correctness. So that part of AI is solved. The only thing that remains is generating proofs, and that is where there's nothing in AI space right now. As soon as we do get something, our mathematical knowledge is going to explode.
> I've been excited about Lean for years, not because of correctness guarantees, but because it opens the door to doing maths using software development methods.
> Libraries of theorems and mathematical objects, with well defined abstractions that are ergonomic to apply in target use cases. Accompanied by good documentation, focused less on how the theorems are proven (how the functions are implemented), and more on what to use them for and how.
How is any of that different from what we had in math before Lean?
It is more software ish. You don't just have a citation to earlier results, you can import the library. And you don't have to trust collaborators as much, the proof engine validates. And you can use github to coordinate large projects with incremental but public progress.
I have proven quite a few theorems in Lean (and other provers) in my life, and the unfortunate reality is that for any non-trivial math, I still have to figure out the proof on paper first, and can only then write it in Lean. When I try to figure out the proof in Lean, I always get bogged down in details and loose sight of the bigger picture. Maybe better tactics will help. I'm not sure.
If anyone is curious about the phenomenon, the second problem in session 7 at https://incredible.pm/ [ ∀x.(r(x)→⊥)→r(f(x)) ⟹ ∃x.r(x)∧r(f(f(x))) ] is one where the proof is straightforward, but you're unlikely to get to it by just fooling around in the prover.
In principle, LLMs can do this already. If you ask Claude to express this in simple words, you will get this translation of the theorem:
"If applying f to things makes them red whenever they're not already red, then there must exist something that is red AND stays red after applying f twice to it."
Now the proof is easy to see, because it is the first thing you would try, and it works: If you have a red thing x, then either x and f(f(x)) are both red, or f(x) and f(f(f(x)) are both red. If x is not red, then f(x) is red. Qed.
You will be able to interact like this, instead of using tactics.
I love the analogy in David Bessis's wonderful book Mathematica (nothing to do with Wolfram). We all know how to tie our shoes. Now, write in words and symbols to teach someone how you tie your shoes. This is what a proof is.
Often even people with STEM degrees confuse what mathematicians do with the visible product of it - symbols and words on a page. While the formalism of mathematics has immense value for precision, and provides a "serialization language" (to borrow a CS analogy), it would be akin to confusing a Toaster with the Toaster manual, or shoelaces with the instructions.
I started having a much easier time with mathematics when I realized and got comfortable with this idea. In hindsight it should've been obvious to me as a programmer - when I'm building something, I don't ideate in terms of individual lines of code, after all
As a former professional mathematician: the benefits mentioned in the article (click-through definitions and statements, analyzing meta trends, version control, ...) do not seem particularly valuable.
The reason to formalize mathematics is to automate mathematical proofs and the production of mathematical theory.
Rado Kirov shows that formalization transforms how mathematicians think about structure and collaboration. My work begins from the same premise, but in the world of programming and system software. I aim to bring formal structure to programming itself, treating algorithms, operating systems, and programming languages as subjects that can be expressed with the same rigor as mathematics.
Elements of Programming presents programming as a mathematical discipline built on structure, logic, and proof. Written in the style of Euclid’s Elements, it defines computation through clear axioms, postulates, and propositions. Each book develops one aspect of programming as a coherent system of reasoning.
Book I establishes identity, transformation, and composition as the foundations of computation.
Book II introduces algebraic structures such as categories, functors, and monads.
Book III unites operational and denotational semantics to show that correctness means equivalence of meaning.
Book IV formalizes capability-based security and verification through invariants and confinement.
Book V connects type theory with formal assurance, explaining how types embody proofs.
Book VI extends these ideas into philosophy and ethics, arguing that software expresses human intention and responsibility.
I want to respond to each of his points one by one
> powering various math tools
I don't think going through a math proof like they were computer programs is a good way to approach mathematics. In mathematics I think the important thing is developing a good intuition and mental model of the material. It's not a huge problem if the proof isn't 100% complete or correct if the general approach is good. Unlike programming, where you need a program to work 99.9% of the time, you have to pay close attention to all the minute details.
> analyzing meta-math trends
I'm highly skeptical of the usefulness of this approach in identifying non-trivial trends. In mathematics the same kinds of principles can appear in many different forms, and you won't necessarily use the same language or cite the same theorems even though the parallels are clear to those who understand them. Perhaps LLMs with their impressive reasoning abilities can identify parallels but I doubt a simple program would yield useful insights.
> Basically, the process of doing math will become more efficient and hopefully more pleasant.
I don't see how his points make things more efficient. It seems like it's adding a bunch more work. It definitely doesn't sound more pleasant.
Say I'm wanting to formalize a proof. How do I know that what I'm writing is actually a correct formulation?
If it gets more complicated, this problem gets worse. How do I know the thing it is checking is actually what I thought it was supposed to check?
I guess this is a bit like when you write a program and you want to know if it's correct, so you write some tests. But often you realize your tests don't check what you thought.
You don't know. Even with the best theorem provers, your definitions are still trusted. The best way I've found to help with this is to keep your definitions simple, and try to use them to do things (e.g. can you use your definition to solve other problems, does it work on some concrete examples, etc).
A point that is maybe not obvious to people who have not done mathematics at a high level or done “new” mathematics, is that often you end of changing your theorem or at least lemmas and definitions while figuring out the proof. That is, you have something you want to prove, but maybe it is easier to proving something more general or maybe your definitions need to change slightly. Anecdotally, during a project I spend perhaps a year figuring out exactly the right definition for a problem to be able to prove it. Of course, this was a very new thing. For well-know areas it is often straight forward, but at the frontier, both definitions and theorems often change as your proceed and understand the problem better.
In a lot of cases you can get far by locally proofreading the definitions.
Trying to formally prove something and then failing is a common way people find out they forgot to add an hypothesis.
Another pitfall is defining some object, but messing up the definitions, such that there's actually no object of that kind. This is addressed by using test objects. So suppose you define what a ring is, then you also prove that real numbers and polynomials are examples of the thing you defined.
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It provides tools for discovering relevant proofs, theorems, etc. Toying around with lean has actively taught me math that I didn't know before. The entire time it catches me any time I happen to fall into informal thinking and start making assumptions that aren't actually valid.
I don't know of any way to extract the abbreviation engine that lean plugins use in the relevant editors for use in other contexts, but man, I'd honestly love it if I could type \all or \ne to get access to all of the mathematical unicode characters trivially. Or even extend it to support other unicode characters that I might find useful to type.
I am curious to try out lean to understand how definitions in lean are able to operationally capture meaning in an unambiguous manner.
[1] https://www.amazon.com/Mathematica-Secret-World-Intuition-Cu...
No formalism is ontologically neutral in the sense that there is always an implied ontology or range of possible ontologies. And it is always important to make a distinction between the abstractions proper to the formalism and the object of study. A common fallacy involves reifying those abstractions into objects of the theory, at least implicitly.
Libraries of theorems and mathematical objects, with well defined abstractions that are ergonomic to apply in target use cases. Accompanied by good documentation, focused less on how the theorems are proven (how the functions are implemented), and more on what to use them for and how. With proper version control and package management.
I believe that all these practices could vastly improve collaboration and research velocity in maths, as much or more than AI, although they are highly complementary. If maths is coding, AI will be much better at it, and AI will be more applicable to it.
They both can be useful or harmful, do to their respective strengths and trade offs.
PAC/statistical learning is good at needles in the haystack problems assuming that the tail losses, simplicity bias, and corpus representation issues are acceptable and you understand that it is fundamentally existential quantification and control for automation bias etc…
Lean is a wonderful collection of concepts and heuristics but due to Rice and Gödel etc… will not solve all problems with software development.
How Gödel’s second incompleteness theorem shows that you can prove anything, without that proof being meaningful is a lens into that.
It is horses for courses, and remember that even in sub-TC total functional programming, proving and arbitrary functions is very hard, while constructing one is far more tractable.
Even those proofs don’t demonstrate semantic correctness. History is riddled with examples of people using powerful tools that elegantly explain flawed beliefs.
The 2009 crash and gaussian copula as an example.
Get all the value you can out of these tools, but use caution, especially in math, where superficially similar similarities often have conflicting conventions, constraints, and assumptions.
Obviously if you problem is ergotic with the Markov property, both will help, but Automated theorem proving and PAC learning will never be a meta theory of the other IMHO.
He is also a serious research mathematician at the top of his game, considered by many one of the best mathematicians alive. This might be biased by the fact that he is such a good communicator, he is more visible than other similarly good mathematicians, but he is a Fields medallist all the same.
This is a recent talk where he discusses putting it together with LLMs (he's somewhat sceptical it'll be revolutionary for producing new mathematics any time soon)
https://www.youtube.com/watch?v=K5w7VS2sxD0
> Libraries of theorems and mathematical objects, with well defined abstractions that are ergonomic to apply in target use cases. Accompanied by good documentation, focused less on how the theorems are proven (how the functions are implemented), and more on what to use them for and how.
How is any of that different from what we had in math before Lean?
I don't see why they would.
If anyone is curious about the phenomenon, the second problem in session 7 at https://incredible.pm/ [ ∀x.(r(x)→⊥)→r(f(x)) ⟹ ∃x.r(x)∧r(f(f(x))) ] is one where the proof is straightforward, but you're unlikely to get to it by just fooling around in the prover.
You will be able to interact like this, instead of using tactics.
I love the analogy in David Bessis's wonderful book Mathematica (nothing to do with Wolfram). We all know how to tie our shoes. Now, write in words and symbols to teach someone how you tie your shoes. This is what a proof is.
Often even people with STEM degrees confuse what mathematicians do with the visible product of it - symbols and words on a page. While the formalism of mathematics has immense value for precision, and provides a "serialization language" (to borrow a CS analogy), it would be akin to confusing a Toaster with the Toaster manual, or shoelaces with the instructions.
The reason to formalize mathematics is to automate mathematical proofs and the production of mathematical theory.
I just released my treatise yesterday, at https://leanpub.com/elementsofprogramming
Elements of Programming presents programming as a mathematical discipline built on structure, logic, and proof. Written in the style of Euclid’s Elements, it defines computation through clear axioms, postulates, and propositions. Each book develops one aspect of programming as a coherent system of reasoning.
Book I establishes identity, transformation, and composition as the foundations of computation.
Book II introduces algebraic structures such as categories, functors, and monads.
Book III unites operational and denotational semantics to show that correctness means equivalence of meaning.
Book IV formalizes capability-based security and verification through invariants and confinement.
Book V connects type theory with formal assurance, explaining how types embody proofs.
Book VI extends these ideas into philosophy and ethics, arguing that software expresses human intention and responsibility.
Deleted Comment
> powering various math tools
I don't think going through a math proof like they were computer programs is a good way to approach mathematics. In mathematics I think the important thing is developing a good intuition and mental model of the material. It's not a huge problem if the proof isn't 100% complete or correct if the general approach is good. Unlike programming, where you need a program to work 99.9% of the time, you have to pay close attention to all the minute details.
> analyzing meta-math trends
I'm highly skeptical of the usefulness of this approach in identifying non-trivial trends. In mathematics the same kinds of principles can appear in many different forms, and you won't necessarily use the same language or cite the same theorems even though the parallels are clear to those who understand them. Perhaps LLMs with their impressive reasoning abilities can identify parallels but I doubt a simple program would yield useful insights.
> Basically, the process of doing math will become more efficient and hopefully more pleasant.
I don't see how his points make things more efficient. It seems like it's adding a bunch more work. It definitely doesn't sound more pleasant.
Say I'm wanting to formalize a proof. How do I know that what I'm writing is actually a correct formulation?
If it gets more complicated, this problem gets worse. How do I know the thing it is checking is actually what I thought it was supposed to check?
I guess this is a bit like when you write a program and you want to know if it's correct, so you write some tests. But often you realize your tests don't check what you thought.
Trying to formally prove something and then failing is a common way people find out they forgot to add an hypothesis.
Another pitfall is defining some object, but messing up the definitions, such that there's actually no object of that kind. This is addressed by using test objects. So suppose you define what a ring is, then you also prove that real numbers and polynomials are examples of the thing you defined.