The way humans do it is via curiosity/boredom/surprise. If we can't predict some thing well (i.e. we're "surprised" by it) then that both acts as learning trigg

The way humans do it is via curiosity/boredom/surprise. If we can't predict some thing well (i.e. we're "surprised" by it) then that both acts as learning trigger to predict better next time, as well as retains our interest to explore it. Eventually AGI's will need to learn by experimentation as we do, but in the meantime ability to well predict a potential training sample could be used to decide whether to add it to training set or not. At the moment it seems the main emphasis is on a combination of multi-modality (esp. video) and synthetic data where one generation of LLM generates tailored training samples for the next generation. I guess this synthetic data allows a more selective acquisition of knowledge than just adding surprising texts found in the wild.