"Like the brain" is a fundamentally wrong-headed approach in my opinion. Biological systems are notoriously inefficient in many ways. Rather than modelling AI systems after the way "the brain" works, I think they should be spending a lot more time talking to philosophers and meditation specialists about how we *think* about things.
What you're suggesting has been the dominant paradigm in AI research for most of the 60-70 odd years there has been AI research. Some people have always thought we should model "thinking" processes, and others though we should model neural networks. At various points one or the other model is dominant.
To me it makes no sense to structure a memory system as inefficiently as the brain's, for example, with all it's tendancy to forgetfulness, omission, and random irrelevant "correlations". It makes far more sense to structure purely synthetic "memories" using database technologies of various kinds.
I have to disagree on it making no sense to structure a memory system "inefficiently" as the brain's, because inefficiency can mean different things. The brain is extraordinarily power efficient and that is an important consideration.
It's most likely, in my opinion, that we will eventually find a happy medium between things that computers do well, like compute and store information exactly, and what humans do well, process efficiently and make associations and correlations quickly.
Sure, biologicial systems employ some interesting short cuts to their processing, but always at a sacrifice in their accuracy. We should be striving for systems that are *better* than the biological, not just similar, but in silicon.
While I don't doubt silicon will be important for the foreseeable future, it does have limitations you know.