[YeGoblynQueenne]

>> The large majority of deployed machine learning algorithms actually can't learn.

Learning algorithms construct a model of data- that's what we call "learning", or "training". The trained model is then used to make decisions, so that's what you would deploy in a production system.

In that sense the deployed systems don't learn any further, yes, but that's because you don't deploy the algorithm per se.

An exception is what is known as "online learning" algorithms, that can continuously update their model as new data becomes available (hance, "online").

[Cybiote]

I took care to point out deployed models. I did not say the models did no learning. In fact, one can draw an analogy from evolution and instinct to offline training/hyperparameter search/architecture tweaks and deployed model.

And, technically, the manner by which most nets are trained, via (minibatched) stochastic gradient descent is online. But the architectures are so ill-adapted to learning continuously and backprop is so data inefficient that retraining on new data is often just better done from scratch (or at best, freezing weights).