bertonvv

It seems that Eric Lu at Cognition AI used the exact same strategy on fewer GPUs to factor RSA-260 a couple weeks ago: https://cognition.com/blog/factoring-rsa-260

Devin (their AI agent) ported CADO-NFS to run on GPUs, similarly without any claimed algorithmic factoring improvements, they just let it run for 13 GPU-years. I recommend reading their article since it's much more thorough on details.

I've been wondering whether AI really is improving rapidly at open problems or we're being fooled.

- OpenAI invites researchers to use their models, in fact giving at least 100,000 researchers free access[1], but there are also those that pay

- Internal OpenAI models are reportedly solving open problems at a surprisingly fast rate[2]

- But researchers will typically work on open problems. A researcher who is using Codex to make progress on open problems will be feeding it fresh training data on precisely the problems the internal models are evaluated on.

- So while it looks like the new models are suddenly solving lots of open problems, they could be significantly piggybacking on human progress, with models "inspired" by the work of researchers from all around the world?

This theory predicts that there'll be many more researchers coming forward just like TFA, as sOpenAI announces more solutions. It doesn't assume all of AI progress is a mirage, just that there's plagiarism.

[1]: https://openai.com/index/chatgpt-for-academic-researchers/

[2]: https://xcancel.com/OpenAI/status/2097374643518640382#m