ImaTigger

Ice making (someone else said refrigeration, but this is more specific).

I know about Smolensky's theories (have probably read that paper, but don't remember it exactly; have def. read others by PS); PS and JE are definitely contemporaries, and work/have worked in similar areas. However, these two theories operate at different levels and time scales. The oscillatory coupling theories of PS et al are related to real time computations carried out by neural networks, whereas the trophic wave theories of JE et al. relate to how these networks come to be organized as they are. As per other posts, both are useful, and probably both true to some extent. Neither is directly applicable yet in a way that makes contact with the cognitive level.

I am a computational cognitive neuroscientist, an have worked at many levels. I find each kind of data and model useful to some extent, but I have to admit that the least useful, are, to my mind, those at the detailed neural network level, like the ones discussing in this paper. Somewhat more useful are higher level dynamic architecture models, and, at the highest level, cognitive models, which constrain the behavioral target we are trying to explain. I personally (as one can tell from my other posts here) find the dynamics brain development models to be the most compelling as overall models, but they are not particularly explanatory at the detailed level. Brain science is trying to do the hardest thing you can imagine, that is, explain the most complex machine in the known universe. We persist, but no one entering this field should have very high expectations of near term grand successes.

See my post in another thread re Elman et al. Rethinking Innateness (https://mitpress.mit.edu/books/rethinking-innateness).

What you're looking for is Elman et al's theory in Rethinking Innateness (https://mitpress.mit.edu/books/rethinking-innateness) It's more like Darwin than Newton, and is (to the point of another post off this thread) an early deep-learning-like theory of how the brain (or at least the cortex) becomes organized.

Found an open copy: http://shrager.org/vita/pubs/2019JLMEIsCancerSolvable.pdf

Re "real insight", quoted from the highlight: "Current medical research tries to minimize risk to the individual while maximizing benefit to society. Yet, in traditional research, no attempt is made to calculate the value to society of what may be gained by research. In contrast, GCTA seeks to maximize both individual and societal benefit based upon explicit quantification for each. One quantitative measure of societal benefit is information gain, which enables the prioritization of one option versus another at a specific moment in time. "

This seems like a recipe for an acquired auto-immune disorder!

"Famously, UPS/FedEx are better than USPS."

This a red herring. First off, USPS is nearly as good as those, and those others don't have nearly the geographical breadth requirements of the USPS.

This is a pretty broad request. esp. not knowing you well enough to be able to tell what level you can handle/want. I suggest going to the local library and skimming the collections. If you have a half-decent local library they'll have a half decent collection in each of these fields.

Middle school is hard because it's the point at which parents are cut out of the educational system, either by virtue of their not being able to be helpful (e.g., they can't do algebra or speak French either!), or by the child (through mis-directed rebellion or overzealous DIYishness).

Coincidentally, I ran in this paper just now, on latin squares (one of the mentioned fields): https://malmskog.files.wordpress.com/2011/10/revised-math-ma...

Elman proposed (and I think built) a model in the mid 1990s (see his book: Rethinking Innateness) that works in exactly this manner: A "wave of growth" moves across an initially highly connected "cortical" network, where parts learn (parcel out), and then become fixated, as other, nearby parts learn. You end up with what amounts to the end-result of what would happen if you built a stack of deep-learned transducers with higher order concepts built in top of lower order ones.

This is sort of a ridiculous list. There are, possibly by definition, and infinitude of under-investigated fields. A more useful list might be a list of OVER investigated fields, such as PvNP, Deep Learning, Consciousness, fMRI, ...