I am not sure that he's the first to note that the brain is a predictive machine, or to notice that models of state or the environment can be formulated entirel

I am not sure that he's the first to note that the brain is a predictive machine, or to notice that models of state or the environment can be formulated entirely in terms of predictions[0]. Personally, I find that idea very convincing, because we already know how to learn to predict (reinforcement learning), and we can use the same framework to optimize an agent's behavior to maximize against these predictions. There is some evidence that the brain uses something like temporal difference methods to learn. The hard parts are: how to do this in a stable and efficient matter, and how to build up our representation to go from relatively simple goals (e.g., survive and collect resources) to more abstract goals (e.g., translate a poem in such a way that the original's charm is preserved[1]). That is, you want to have a system that is capable of climbing up Maslow's hierarchy (or something like it) and actually proceeds to do so based on emergent properties of the system itself. Proposing and investigating new ways of constructing learning systems so that there is a continuum between primal drives and complex abstract goals is hard, and even if grid cells are an epiphenomena, then it is worth studying them to see how they might develop from simpler rules. If Hawkins' team can make something useful, then that can still be beneficial by allowing for more powerful and flexible AI, even if it's not a theory of everything. --- 0. See for example: http://web.eecs.umich.edu/~baveja/Papers/psr.pdf http://web.eecs.umich.edu/~baveja/Papers/psr.pdf (Littman, Sutton, and Singh), which describes and analyzes the equivalence between belief-state based representations and state representation based on predictions.