2 claims in “machine learning, artificial intelligence”
Large neural network models can always be decomposed into small specialized models, each solving specific tasks equally well, because the large model is itself just a gigantic ensemble of small task-tailored models addressing an escalating number of tiny problems.
Synthetic data must be validated against real data to ensure the distribution of synthetic outputs matches the real distribution, otherwise the model risks compounding bias or error.