[adw]

> generative grammar/ symbolic approaches were pretty much left behind by NN methods

Which is the same thing as hand-engineered feature stacks being left behind in vision problems, really. The story in every field is more or less "you're not clever enough to engineer good features"; "you might be clever enough to define good symmetries for the feature space in which the features live... maybe" (convolutional neural networks in image problems); "... but maybe not even that" (attention mechanisms).

[snovv_crash]

The hand generated features are still superior for SfM style problems, where the geometry is well defined but would need to be learned by the NN from scratch.