I really love your critique, because it seems consonant with the work my lab does. We work with EM neuron images, we have a DNN trained on image patches that ar

I really love your critique, because it seems consonant with the work my lab does. We work with EM neuron images, we have a DNN trained on image patches that are quite small as the training cost is quite high. If I recall correctly, it was trained for months using 7x7x7 patches on several 100^3 voxel volumes. The output of the network was fractured, and at the time it was the best we could do. However, humans, because we can see the bigger picture, bigger than 7x7 patches on 2D image displays at a time, we can resolve the splits in the image segmentation that the AI is bluntly unable to. It's worth noting that my lab uses convolutional neural networks (CNNs) not recurrent neural networks (RNNs). It seems to me that this is a technical problem of being able to train and run a large enough network to approach human abilities in pattern recognition. Sometimes this is easier than others. However, a disclaimer, though I hang out with machine learning people, I'm not yet one myself.