I have seen some ML methods that learn explicit structural mappings [1][2]. I think ML is great when used as such. Even RL can work, because it operates over di

I have seen some ML methods that learn explicit structural mappings [1][2]. I think ML is great when used as such. Even RL can work, because it operates over discrete spaces. But casting any problem with a discrete correct answer into floating point calculations that mimic probability is a bad idea. [1] : Learning to Compose Neural Networks for Question Answering [2] : Inferring and Executing Programs for Visual Reasoning