Comment by don_esteban
1 day ago
Leaving aside the (much more important) issue of monopolizing intelligence, and assuming the claimed result is indeed correct, the question is whether a) the correctness proof is indeed too complex to be understood by humans, or b) the creator (the LLM) is not able to write it down in a form digestible by humans.
Observing the LLM output on problems that have clear solutions invented by humans, I tend to believe that too many people are jumping to conclusions too soon and interpreting b) as a).
If the LLMs are truly so great, we should see (in a short time, i.e. in order of months) a transformation of the math proofs generated by LLMs from the current state of indigestible mess to something that indeed looks like written by human for humans.
Only then would one be able to claim that we got into situation a).
Why should we expect this? Does it help the corporation’s bottom line? How much? Is that worth the R&D spend and compute budget it would need? What advantage do they stand to gain by demystifying their product, that they don’t already get from e.g. Lean verification? Do they gain more if the same research and compute spend on their inscrutable intelligence is directed into making it more intelligent, instead of less inscrutable?
1) Higher-level publicity stunt.
"Our previous version was super-smart, but a bit autistic, even the smartest mathematicians had problems understanding it. The new version is not only super-smart, but can also efficiently communicate its ideas, so that they can be used in real life."
2) Actually, try to use the AI's smarts to gain in real life (not only convincing others to pay you, but actually do the smartest things). Hmmmm, scratch that, that is too idealistic. Use the AI's smarts to become the strongest meanest bastard the others will have no choice but to obey, or else.
Many corporations (their actual customers they're broadcasting to when they say how smart their models are) have requirements (externally imposed, so not something they can just change) that all code is reviewed, so they need it to be understandable, which is basically the same problem.
A more practical approach:
If a paper/proof is impossible to understand by humans, it could be considered useless (irrelevant) for most intents & purposes.
Regardless how interesting or valid looking, might as well be gibberish pulled from /dev/random. Until:
a) Written down in a form that humans can understand. And/or
b) Turned into practical applications that are demonstrated to work.
In other words: safe to ignore, until a) and/or b) occurs.
Well, yes, in theory.
If any of those papers were sent to a scientific conference, they would have been rejected as inscrutable gibberish: "resubmit once the presentation is significantly improved".
Yet, mathematicians spend their time trying to understand them.
Real life prisoner's dilemma.