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И вот ещё про факторы из https://svail.github.io/mandarin/ -- ещё одно свидетельство (при)остановки прогресса в этой области: "3.4. The limiting factor is data Time spent on a machine-learning problem roughly falls into the following three categories: getting data, developing algorithms, and training models. One of the reasons deep learning has been so valuable is that it has converted researcher time spent on hand engineering features to computer time spent on training networks. The end-to-end learning approach for speech recognition further reduces researcher time. GPUs have added so much value because they have reduced the training time. The systems work our team has done to speed up neural network training has further reduced that. We can now train a model on 10,000 hours of speech in around 100 hours on a single 8 GPU node."