"research reinforces that solve or simplify, or integral is losing 
competition"

I really don't think so. It's way easier to use a computer algebra system 
to do the things that these functions do than use Lean. Every step in Lean 
has to be "handwritten" by the user as its an interactive theorem prover, 
i.e. it has to be justified by some appeal to a ring axiom or something. 
Simplifying algebraic expressions in Lean is much more tedious than just 
calling "simplify" in SymPy because SymPy will do a lot of things 
automatically. As well, Lean works by using "goals", i.e. you have to 
pre-specify the end result, as its made to prove theorems. Much more could 
be said but overall my point is that Lean and SymPy are meant for different 
purposes.

And for what it's worth, ChatGPT Premium uses SymPy as a backend if asked 
to do mathematical tasks because a computer algebra system is guaranteed to 
be correct.
On Sunday, August 18, 2024 at 11:26:52 PM UTC-4 peter.st...@gmail.com wrote:

> I am not a trained mathematician.
> My question: would Gödel's incompleteness theorem not set a limit to what 
> can be proven 'automatically'? 
>
> syle...@gmail.com schrieb am Montag, 19. August 2024 um 02:34:15 UTC+2:
>
>> AI achieves silver-medal standard solving International Mathematical 
>> Olympiad problems - Google DeepMind 
>> <https://deepmind.google/discover/blog/ai-solves-imo-problems-at-silver-medal-level/>
>>
>> Recently, Google had announced the result that their AI model, AlphaProof 
>> and AlphaGeometry can silver medal in IMO problems. Their system is hybrid 
>> of symbolic models, and uses proof assistant Lean as backend, which 
>> guarantees that the proof can be verified automatically. 
>> ChatGPT had many problems that it can hallucinate the steps of proof, and 
>> keep human verifying their result, as well as understaing the steps, so 
>> expressing proof as formal proof statements is a gain.
>>
>> I think that the research reinforces that solve or simplify, or integral 
>> is losing competition. Because a lot of them are written with heuristics 
>> that won't win with AI, and we also have concerns about code around them 
>> are getting messy.
>>
>> I think that if we want to avoid the losing competition, and make AI 
>> systems work collaborative, symbolic computation should be focused to solve 
>> only a few 'formal' problems in 100% precision and speed. 
>>
>> I already notice that there is research to connect Wu's method to 
>> AlphaGeometry
>> [2404.06405] Wu's Method can Boost Symbolic AI to Rival Silver Medalists 
>> and AlphaGeometry to Outperform Gold Medalists at IMO Geometry (arxiv.org) 
>> <https://arxiv.org/abs/2404.06405>
>> Although symbolic system would no longer competitive solution to general 
>> math problems, the 'formal' symbolic systems can still be valued. (I also 
>> hear that AlphaGeometry2 is using Wu's method, but I'm trying to verify the 
>> sources)
>>
>> I also think that such advances in AI systems can raise concerns about 
>> software engineering careers, or educational system, which may be 
>> interesting for some readers in the forum.
>>
>> For example, math exams can be pointless in the future, even to identify 
>> and train good science or engineers in the future, because trained AI 
>> models can beat IMO. I think that in AI age, the education should change, 
>> such that it is not bearing through boring and repetitive systems, which 
>> does not even reflect the capability of future engineers or scientists.
>>
>> Also, I notice that software engineering is changing, because AI models 
>> can complete a lot of code, and precision is improving, or people are 
>> improving the skills of prompting. 
>> It also seems to be deprecating code sharing efforts for open source 
>> communities, because code can be generated rather than shared.
>>
>

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