AI's reliance on synthetic data can create "intersectional hallucinations," leading to unrealistic and potentially dangerous outcomes. These errors highlight the risks of AI misinterpreting complex human data relationships.
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.
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.