konmok
Joined 1 year ago
Joined 1 year ago
Which studies? [edit: I'll assume you mean these two given by @dorolow: https://arxiv.org/abs/2404.01413 https://arxiv.org/abs/2406.07515]
> It can't be used as the -only- data source of course, but that's not how it is being used
Right, so human data creation would also have to scale up exponentially, and that's not gonna happen.
> because there's no difference between synthetic data and the data people can create
I mean, that's obviously false, otherwise model collapse wouldn't exist. The difference is statistical, but it's there.
> It is accelerating how quickly researchers and engineers can do their jobs. > https://news.mit.edu/2026/ai-helps-design-new-materials-that...
That's pretty clearly a hype article, the headline even says "The CrysVCD tool developed at MIT COULD cut the huge amounts of time and money spent". I'm asking for empirical measurements of timelines, not hypotheticals.
> This is only the beginning, too... Look ahead a year or two.
Lol that excuse is getting really old
No it can't? Every time the labs try this we see model collapse, e.g. shoving goblins into every conversation.
And I have seen zero evidence that AI is accelerating materials science in any meaningful way, let alone photonic computing.
Plus, we have no real reason to think LLMs are anywhere close to AGI or ASI. So arguments like these are just distracting from the very real, very present danger that LLMs pose: information breakdown, societal collapse and environmental destruction. In other words, this is criti-hype.
https://www.newsweek.com/map-data-centers-built-drought-hit-...