All that is only needed because incremental learning algorithms don't really work all that well. It's a dirty secret in the field that we still don't have good answers for catastrophic forgetting in neural networks (the best candidate incremental learner as of right now), and the other alternatives are far worse.

This is good to have but it doesn't really address the problem of predictive accuracy in the presence of nonstationarity. The safeguards just help us switch off the model at the right time. We're still stuck with no capability in the new environment.

I think knowing what you don't know is still a pretty big win. It can help people trust the models in cases they do work, and it can serve as a diagnostic for why it fails in certain circumstances.

This sounds an awful lot like guassian processes which are fairly common in research environments. I don't know how common it is to deploy guassian processes in the real world, but I see published papers integrating them into other models all the time. The gist is that instead of input -> prediction you get input -> prediction + sigma (every prediction is given as a guassian distribution).

For common neural network models, the output probability has no meaning for out of distribution inputs, so you need to do an ensemble or some other method to get at the actual model confidence. I don't know enough about Gaussian processes to know if they have any limitations like that.

But it's an interesting point, e.g. if a CNN works better on in-distribution data, but a Gaussian process is better at providing a confidence estimate for OOD points (if it does), a hybrid model is possible.