@abetusk 12d
P != NP is more likely akin to a fundamental mathematical law, like Noethers theorem [0]. No amount of self improving AI will alter the fundamental laws of physics. More provincially, in our solar system, there's a cap on energy so that any system that uses it will eventually hit a ceiling (like a self improving AI that eats up energy resources at an exponential pace).

As motivation for why P != NP, one can think of it as a finite restatement of the Halting Problem [1] in the form of asking whether a (polynomially sized) Turing machine has an input that will halt in K steps.

[0] https://en.wikipedia.org/wiki/Noether%27s_theorem

[1] https://en.wikipedia.org/wiki/Halting_problem

@ufo 12d
The catch here is that P vs NP talks about worst-case behavior. Many SAT solver inputs can be solved quickly with good heuristics, but there are some that are still difficult no matter what you do. One way to see this is via the phase transition behavior of SAT.


If you have too few constraints it's easy to find a solution to the problem. If there are too many constraints it's also easy to show there are no solutions. But in the middle there's a sweet spot in the ratio of constraints to variables that's when the problem becomes fiendishly difficult.

@adwn 12d
> but not be able to figure out what pattern of bits gets a bunch of AND and OR gates to output a "1"? 10-15 years ago, the basic idea was "P≠NP because otherwise computers could do crazy shit." Well, looks like they can do crazy shit!

I think you've fundamentally misunderstood the meaning of the P!=NP problem. In very simplified terms, it's not about what a computer can do, but about how long it takes to do something in the worst case.

@sirwhinesalot 12d
There already exist local-search based algorithms that can find solutions way faster than a SAT solver can... Or they get completely stuck unable to make any progress.

All an LLM does is guess the next token based on the previous tokens and its training weights. For it to give you a solution to a large SAT problem it'll have to spit out a million character long binary string.

The likelyhood most of that string will be entirely hallucinated is very high. LLMs are not magic.

Deep Learning is already used internally in some SAT solvers to heuristically pick in which direction the search should go.

@ftxbro 12d
> "10-15 years ago, the basic idea was "P≠NP because otherwise computers could do crazy shit."

Everyone is downvoting you because that's not the mathematical explanation, but it's absolutely the informal explanation that the 'pop science' ones were saying.

@bigbillheck 12d
> isn't everyone expecting some kind of superintelligent AGI that can recursively improve itself to turn the planet into goo?

I most certainly am not.