Hacker News: OneManyNone

New comment by OneManyNone in "More questions about whether researchers can trust OpenAI with unpublished math"

OneManyNone — Thu, 10 Sep 2026 16:47:36 +0000

“ On Tuesday, September 1, we heard rumors that two Millennium Prize problems had been resolved. Inspired by these rumors and by the step change in performance of our internal model, we launched an effort to evaluate it on all open Millennium Prize problems and a few other high-impact problems.”

- https://openai.com/index/navier-stokes-solution/

They do not explicitly admit to knowing about NS specifically, but are extremely explicit that they tried to scoop some potential millennium prize winners.

New comment by OneManyNone in "Detecting LLM-Generated Texts with “Classical” Machine Learning"

OneManyNone — Thu, 16 Jul 2026 18:51:57 +0000

All of that may be true, but pangram currently has a false positive rate of about 1 in 10000, and this has been tested by feeding in thousands of texts written before 2020.

That may not last if AI companies start trying to build models that fool it, but for the time being at least, modern models do have strong tells.

New comment by OneManyNone in "Claude's Cycles [pdf]"

OneManyNone — Wed, 04 Mar 2026 21:12:02 +0000

That’s fair. Clearly Knuth himself thought it was impressive, that’s a strong signal.

New comment by OneManyNone in "Claude's Cycles [pdf]"

OneManyNone — Tue, 03 Mar 2026 20:19:23 +0000

Claude did not find a proof, though. It found an algorithm which Knuth then proved was correct.

New comment by OneManyNone in "I want to wash my car. The car wash is 50 meters away. Should I walk or drive?"

OneManyNone — Tue, 17 Feb 2026 15:00:56 +0000

The companies aren’t changing anything. LLM outputs are just more random than people realize. Run the same prompt 10 times if you really want to know how well they can answer.

New comment by OneManyNone in "Scientists working to decode birdsong"

OneManyNone — Mon, 21 Oct 2024 22:18:08 +0000

Counterpoint: What progress has generative linguistics made in the same amount of time that deep learning has been around? It sure doesn't seem to be working well.

Also, the racecar example is because of tokenization in LLMs - they don't actually see the raw letters of the text they read. It would be like me asking you to read this sentence in your head and then tell me which syllable would have the lowest pitch when spoken aloud. Maybe you could do it, but it would take effort because it doesn't align with the way you're interpreting the input.

New comment by OneManyNone in "Scientists working to decode birdsong"

OneManyNone — Mon, 21 Oct 2024 22:14:30 +0000

I think this is greatly complicated by the fact that the human brain has been "pre-trained" (in the deep learning sense) by hundreds of millions of years of evolution.

A pre-trained LLM also can also learn new concepts from extremely few examples. Humans may still be much smarter but I think there's a lot of reason to believe that the mechanics are similar.