Prediction/compression seems to be working out as a path to general intelligence, implicitly representing situations in terms of their key legible features, making it easy to formulate policies appropriate for a wide variety of instrumental objectives, in a wide variety of situations, without having to adapt the representation for particular kinds of objectives or situations. To the extent brains engage in predictive processing, they are plausibly going to compute related representations. (This doesn’t ensure alignment, as there are many different ways of making use of these features, of acting differently in the same world.)
Prediction/compression seems to be working out as a path to general intelligence, implicitly representing situations in terms of their key legible features, making it easy to formulate policies appropriate for a wide variety of instrumental objectives, in a wide variety of situations, without having to adapt the representation for particular kinds of objectives or situations. To the extent brains engage in predictive processing, they are plausibly going to compute related representations. (This doesn’t ensure alignment, as there are many different ways of making use of these features, of acting differently in the same world.)
Yes, predictive processing as the reason behind related representations has been the interpretation in a few papers, e.g. The neural architecture of language: Integrative modeling converges on predictive processing. There’s also some pushback against this interpretation though, e.g. Predictive Coding or Just Feature Discovery? An Alternative Account of Why Language Models Fit Brain Data.