Suppose there are many times more posts about something one generation of LLMs can't do (arithmetic, tic-tac-toe, whatever), than posts about how the next generation of models can do that task successfully. I think this is probably the case.
While I doubt it will happen, it would be somewhat funny if training on that text caused a future model to claim it can't do something that it "should" be able to because it internalized that it was an LLM and "LLMs can't do X."
The new model does play very well but when it draws the board it frequently places the moves in incorrect locations (but seemingly still keeps track of the correct ones). But I can't fault it too much, I don't think what is essentially ASCII art is intended to be a strength of the model.
Edit: Actually third game with it led to it making an illegal move, and claiming a draw (which would've been inevitable given optimal play for the rest of the game but there were several valid moves left to make).
Also a concern about the paper generation process itself:
> In a similar vein to idea generation, The AI Scientist is allowed 20 rounds to poll the Semantic Scholar API looking for the most relevant sources to compare and contrast the near-completed paper against for the related work section. This process also allows The AI Scientist to select any papers it would like to discuss and additionally fill in any citations that are missing from other sections of the paper.
So... they don't look for related work until the paper is "near-completed." Seems a bit backwards to me.
They evaluate their automated reviewer by comparing against human evaluations on human-written research papers, and then seem to extrapolate that their automated reviewer would align with human reviewers on AI-written research papers. It seems like there are a few major pitfalls with this.
First, if their systems aren't multimodal, and their figures are lower-quality than human-created figures (which they explicitly list as a limitation), the automated reviewer would be biased in favor of AI-generated papers (only having access to the text). This is an obvious one but I think there could easily be other aspects of papers where the AI and human reviewers align on human-written papers, but not on AI papers.
Additionally, they note:
> Furthermore, the False Negative Rate (FNR) is much lower than the human baseline (0.39 vs. 0.52). Hence, the LLM-based review agent rejects fewer high-quality papers. The False Positive Rate (FNR [sic]), on the other hand, is higher (0.31 vs. 0.17)
It seems like false positive rate is the more important metric here. If a paper is truly high-quality, it is likely to have success w/ a rebuttal, or in getting acceptance at another conference. On the other hand, if this system leads to more low-quality submissions or acceptances via a high FPR, we're going to have more AI slop and increased load on human reviewers.
I admit I didn't thoroughly read all 185 pages, maybe these concerns are misplaced.
> Artists and "creative" people have long held a monopoly on this ability and are now finally paying the price
I've seen a lot of schadenfreude towards artists recently, as if they're somehow gatekeeping art and stopping the rest of us from practicing it.
I really struggle to understand it; the barrier of entry to art is basically just buying a paper and pencil and making time to practice. For most people the practice time could be spent on many things which would have better economic outcomes.
> monopoly
Doesn't this term imply an absence of competition? There seems to be a lot of competition. Anyone can be an artist, and anyone can attempt to make a living doing art. There is no certification, no educational requirements. I'm sure proximity to wealth is helpful but this is true of approximately every career or hobby.
Tangentially, there seem to be positive social benefits to everyone having different skills and depending on other people to get things done. It makes me feel good when people call me up asking for help with something I'm good at. I'm sure it feels the same for the neighborhood handyman when they fix someone's sink, the artist when they make profile pics for their friends, etc. I could be wrong but I don't think it'll be entirely good for people when they can just have an AI or a robot do everything for them.
This is also one of the first things I test with new models. I did notice that while it still plays very poorly, it is actually far more consistent with the board state, making only legal moves, and noticing when I win than is GPT4o.
Suppose there are many times more posts about something one generation of LLMs can't do (arithmetic, tic-tac-toe, whatever), than posts about how the next generation of models can do that task successfully. I think this is probably the case.
While I doubt it will happen, it would be somewhat funny if training on that text caused a future model to claim it can't do something that it "should" be able to because it internalized that it was an LLM and "LLMs can't do X."