OpenAI researcher Mark Selke describes breakthroughs by OpenAI's mathematical reasoning model, Astra, improving upon historical Kabatiansky-Levenshtein bounds in coding theory.
Opinion
Selke: Proving mathematical results is becoming much less of a bottleneck
“Proving the result was, like, so hard that kind of the other stuff was just kind of coming along for the ride, right? You know, like, if you manage to, like, prove this thing yourself, you're automatically gonna understand it quite well. You're kind of respons…”
Prediction Not checkable as stated
Selke: AI might plausibly never solve problems like P versus NP
“Even if AI get, you know, continues getting, like, exponentially better at math, like, it might, you know, plausible will never solve something like P versus NP.”
Disclosure
Selke: Coding theory was Astra's only proof requiring human interaction
“This was the one case where there was some interactivity involved. So for all of, so except for this pair, it was just, you know, we had some problems, we fed them in, and we you know, the model came back with some solutions.”
Insight
Selke: AI models consistently nail detailed mathematical execution where humans get lost
“Another relative strength that's pretty noticeable is just, like, it's very good at executing on some, like, idea once it has it. Like, you know, whenever you have an idea, there's, like, There's usually some amount of, you know, getting everything lined up, l…”
Insight
Selke: AI avoids human cognitive bias by easily resetting polluted context
“Like, as a human, if you have some, like, wrong path you go down for a while, it can be hard to, like, rewire your brain to, like, start over and, like, try a different path. Like, you're kind of, the initial idea is kind of linked in your brain with these oth…”
Assertion Supported
Selke: OpenAI's Astra proved that a non-sofic group exists
“So, so the result that Astra proved is simply that there exists a non-sulfic group.”