I used an LLM to generate an inkblot that translates to a Python string and number along with verification of it, which just proves that it is possible.
the way that the quoted article creates Perl programs is through OCRing the inkblots (i.e. creating almost random text) and then checking that result to see if said text is valid Perl
it's not generating a program that means anything
> it's not generating a program that means anything
Glad we agree.
[1] Could OCR those inkblots (i.e. they are almost random text)
OCRing literal random inkblots will not produce valid C (or C# or python) code, but it will prodce valid Perl most of the time, because Perl is weird, and that is funny.
It's not about obfuscating text in inkblot, it's about almost any string being a valid Perl program, which is not the case for most languages
Edit0: here: https://www.mcmillen.dev/sigbovik/
> it's about almost any string being a valid Perl program
Is this true? I think most random unquoted strings aren't valid Perl programs either, am I wrong?
Because of the flexibility of Perl and heavy amount of symbol usage, you can in fact run most random combinations of strings and they’ll be valid Perl.
Copying from the original comment: https://www.mcmillen.dev/sigbovik/
What percent of all e.g. ASCII or Unicode strings are valid expressions given a formal grammar?
Ironically LLMs seem pretty bad at writing AppleScript, I think because (i) the syntax is English-like but very brittle, (ii) the application dictionaries are essential but generally not on the web, and (iii) most of the AppleScript that is on the web has been written by end users, often badly.
I think as of the last Stack Overflow developer survey, it only had ~4% market share…
I say this as an R user who spams LLMs with R on a daily basis.
Here's a link to the model if you want to dive deeper: https://huggingface.co/philomath-1209/programming-language-i...
I mean you can go read his blog post that's pinned where he argues that there's no new ideas and it is all data. He makes the argument that architecture doesn't matter, which is just so demonstrably false that it is laughable. He's a scale maximalist.
I also expect an AI researcher from a top university to not make such wild mistakes
> 3. RLHF: first proposed (to my knowledge) in the InstructGPT paper from OpenAI in 2022
| Specifically, we use reinforcement learning from human feedback (RLHF; Christiano et al., 2017; Stiennon et al., 2020) to fine-tune GPT-3 to follow a broad class of written instructions (see Figure 2).
|| Our algorithm follows the same basic approach as Akrour et al. (2012) and Akrour et al. (2014)
I'm sure we can trace the idea back to the 80's if not earlier. This is the kind of take I'd expect a non-researcher to have, but not someone with two dozen NLP papers. The Instruct-GPT paper was just the first time someone integrated RLHF into a LLM (but not a LM).
Maybe a better article is the one he wrote on Super Intelligence From First Principles. As usual, when someone says "First Principles" you bet they're not gonna start from First Principles... I guess this makes sense in CS since we index from 0
[0] https://arxiv.org/abs/2203.02155
[Christiano et al] https://arxiv.org/abs/1706.03741
[Stiennon eta al] https://arxiv.org/abs/2009.01325
[Akrour et al (2012)] https://arxiv.org/abs/1208.0984
As a result, OP seems to think the model was trained on a lot of Perl: https://xcancel.com/jxmnop/status/1953899440315527273#m
LOL! I think these results speak more to the flexibility of Perl than any actual insight on the training data! After all, 93% of inkblots are valid Perl scripts: https://www.mcmillen.dev/sigbovik/