alt Hacker News> Every time the labs try this we see model collapse
The latest studies demonstrate model collapse is not a given and synthetic data can be used just fine. The latest models are proof of that, they're all trained on large swathes of synthetic data. It can't be used as the -only- data source of course, but that's not how it is being used. This is an obvious conclusion, too, because there's no difference between synthetic data and the data people can create, the difference is whether that data is revealing new information about the thing the model is trying to learn. If the synthetic data is just teaching the model the same thing over and over again it results in overfitting, so it needs to be done intelligently.
For example, if I have an example of a puzzle, I can generalize that example and create thousands of synthetic data examples, with different rotations/perspectives, rather than having to find the data naturally. It's not that the models are just generating data out of thin air, they're generating the synthetic data on top of real world data. The smarter the models get, the better they are at generating quality synthetic variations and finding valid synthetic variations.
> And I have seen zero evidence that AI is accelerating materials science in any meaningful way, let alone photonic computing.
It is accelerating how quickly researchers and engineers can do their jobs.
https://news.mit.edu/2026/ai-helps-design-new-materials-that...
This is only the beginning, too... Look ahead a year or two.
> The latest studies demonstrate model collapse is not a given
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