However, training these AI models needs a LOT of data, so much that some of it has to be synthetic – not real data from real people, but data that reproduces existing patterns. Most synthetic datasets are themselves generated by Machine Learning AI.
Maybe having a blind man teach another blind man how to see based on how he imagines seeing works is a recipe for disaster...
Turns out analogies are not the actual thing they're analogizing, though. Synthetic data - when properly created and curated - has proven to be very useful and effective in training AI.
Except when it's not, or else this article would have no point.
ETA: also, it seems like a terrible idea to train science models on data you essentially invented. The reason science works is because it follows the extant evidence, not the other way around.
It's not doing scientific discovery though it's doing analysis of known facts. You're just trying to get the AI to ingest that knowledge and to crystallize it.
Think about doing flashcards when revising for an exam, it's the same process really, the same information presented in a different way, in the hope that one of those ways, sticks.
You need to give the AI new data for it to learn anything new (which isn't surprising when you think about it), but for it to just get better at internalizing existing knowledge synthetic data works quite well, and since AI start off knowing nothing most of what you want them to learn is in fact already established knowledge.
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