Humans have much more compressed models, trying to transfer human learning to machine learning could potentially be a way to get more efficient models.

The way we do that currently is by labeling data for training, but maybe there are better ways to do it. Like some semi code to write with hints for the model. Like, instead of labeled data, could have a series of "lectures" of labeled data that would lead to a good end state, instead of training on all the data in parallel.

You don't teach a child calculus by showing them a million calculus problems after all, you ramp up starting with simple numbers and then slowly ramping up with more concepts. But to do that we would need to change how we train models.

Edit: By doing it that way you could see the skill of the model after each lecture, and update the lecture to try to make the model learn better. Not sure how to do that, but such ways to work with parts of models is a potential way forward.

Might be a worthwhile tradeoff. Less efficient model but a clear control interface, versus more efficient model but it's a blackbox.