Interaction in a sense acts as a method of preventing over-fitting. If you can't interact and are given a fixed batch to learn from you can find trivial overfit

Interaction in a sense acts as a method of preventing over-fitting. If you can't interact and are given a fixed batch to learn from you can find trivial overfitted good predictive models (e.g. the identity model). One way of reproducing this aspect of interactions is simply using standard ML techniques to prevent overfitting, such as cross validation. There's another aspect that's more difficult to reproduce that is the "online learning" aspect of interactions. If you can interact, you can form hypothesis in real time, test and modify them. This can greatly enhance learning efficiency I suppose -- you may directly explore fails in your models and improve in an optimal way. This aspect also might be reproduced I believe simply through a large enough dataset. The learner could be given some capability to explore this dataset in a non-sequential way and look for informative results in it. Interesting stuff.