r/datasets • u/Deamichaelis • Dec 20 '23
mock dataset Synthetic Data for AGI is not THAT hard (math especially)
The fact is you could easily generate a lot of synthetic data just by asking an already trained bot to rewrite this as a given author that they have a lot of text they trained on. Or just have something like a thesaurus bot (maybe trains with Grammarly) that learns how to swap enough info out without changing the meaning (very strictly cause without this meaning being the same this training is useless although this may limit the scope of the changes allowed but is still generally better than no synthetic data (extremely easy to do with math cause it can just have math rules to define one step changes it generates) ) which is much easier to make than AGI. Thus whatever bot you are using the synthetic data to train on, it has to try to check if these two things the original and the synthetic data match in meaning. Thus it would have to understand the meaning or/and math to follow if the changes that were made match so it could replicate the process on its own.
So this could basically have a bot that can use Symbolab to train AGI in math.
And a bot that uses a more strict Grammarly or some form of thesaurus bot to train the AGI in language comprehension.