this post was submitted on 17 Jul 2023
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the thing is, each ai is usually trained from scratch. There isn't any easy way to reuse the old weights. So the primary training has been done.... for the existing models. Future models are not affected by how current ones were trained. They will either have to figure out how to keep ai content out of their datasets, or they would have to stick to current "untainted" datasets.
There is! As long as the model structure doesn't change, you can reuse the old weights and finetune the model for your desired task. You can also train smaller models based on larger models in a process called "knowledge distillation". But you're right: Newer, larger models need to be trained from scratch (as of right now)
But even then it's not really a problem to keep ai data out of a dataset. As you said: You can just take an earlier version of the data. As someone else suggested you can also add new data that is being curated by humans. If inbreeding actually ever happens remains to be seen ofc. There will be a point in time where we won't train machines to be like humans anymore, but rather to be whatever is most helpful to a human. And if that incorporates training on other AI data, well then that's that. Stanford's Alpaca already showed how ressource effective it can be to fine-tune on other AI data.
The future is uncertain but I don't think that AI models will just collapse like that
tl;dr beep boop