It's very black and white right now (no knowledge or very constrained knowledge). The same problem exists with just plain old parameter learning, with DL models having so many free parameters it can make training more computationally expensive then it needs to be. For instance, I want to train a reinforcement learner to do some task in the real world (eg. a robot). It would be nice to be able to define a prior that puts very low to no probability on actions that are not physically possible. This would constrain the search space considerably. But it's not really clear how to do this with neural nets.