This suggests that the effective number of parameters is far lower than the nominal number. My head canon for neural networks as overparametrized models still holds.
The latest research suggests Linear A and Linear B is the same script, but used for different languages analogous to how the Latin script is both used for say English and Polish, but with language specific adjustments.
I have applied it to the names in a population database. It learnt interesting, and expected structure. Visualized with UMAP it clustered by gender first, and then something that probably could be described as cultural origin of name.