This is known as the data processing inequality. Non-invertible functions can not create more information than what is available in their inputs: https://blog.blackhc.net/2023/08/sdpi_fsvi/. Whatever arithmetic operations are involved in laundering the inputs by stripping original sources & references can not lead to novelty that wasn't already available in some combination of the inputs.
Neural networks can at best uncover latent correlations that were already available in the inputs. Expecting anything more is basically just wishful thinking.
Neural networks can at best uncover latent correlations that were already available in the inputs. Expecting anything more is basically just wishful thinking.