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> And then there’s also this startling progress that A.I. has been making in solving math problems that no normal people understand, but that even mathematician
by hypfer 9d ago
> And then there’s also this startling progress that A.I. has been making in solving math problems that no normal people understand, but that even mathematicians who do understand the problems find to be important.
Reading this sentence, I've wondered:
Were the reactions similar back when computers were new?
There's this new tech that greatly accelerates stuff. And through that, what was previously considered hard to solve got easier to solve.
Wasn't it always the case that technology suddenly did faster than experts limited to the technology of their time did?
And didn't those experts always think that the problems it could solve are important?
Why else build the technology? Is that not why we're building this in the first place?
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The only real difference I see is this "sentience" stuff leading to the whole "omg it does things on its own", and that by now has been discussed, debunked and flamewared over ad-nauseam.
Can we please stop.
The only thing sentient, making decisions and - most importantly - being accountable is whoever prompts the thing.
If it prompts itself, then that's the person that built the loop software that does that.
It really is not that hard.
If whoever does that doesn't want to take accountability then we have solutions for that. They're called "a large stick".
- kittikitti 9d agoThere were a subset of problems in math that could only be brute-forced by computers because the number of possibilities are too much for humans to reliably calculate and then verify. An early example of this was Euler’s Sum of Powers Conjecture that was disproven (1966) and then The Boolean Pythagorean Triples Problem proven more recently (2016). A more practical example is prime number generation. These all were because of computer algorithms and I don't know why they are not discussed more in the context of AI solving math. The number of decidable math problems that brute-force algorithms can solve have shrunken smaller and smaller every year because of the increases in processing capabilities. We could frame AI generated solutions to yet unsolved math problems as the next generation of these algorithms.