Earlier quoted context omitted.
Deep neural networks aren't aware of changes in their own performance, because their cannot remember or compare their own current performance to the one in the past. Thus humans at least cannot be pure DNN ML algorithm. RNNs have error propagation thru time, but connections' weight changes aren't represented as neuron activations and therefore aren't accessible to the neural network itself. That is RNNs aren't aware…
Deep neural networks aren't aware of changes in their own performance, because their cannot remember or compare their own current performance to the one in the past. What are you talking about? Awareness of the changes is the point of such techniques as momentum or early stopping.
You can train dialogue NLP system as much as you wish, but you'll never get answers to a question "Have you learned this training set already?" which correlate with whether performance on the test set have plateaued.
I wouldn't call it awareness. We are certainly aware that one part of the system monitors another part, the system itself isn't.