I felt like every subject I took in school was like this. We started at the beginning of time, then worked through all the important people and discoveries, etc. But, never, did we reach the state-of-the-art. That was very discouraging.

When learning On my own, I simply start with today, the fill in history as necessary. I don't know FORTRAN or COBOL, for example. There are other examples where this is costing me, but I can't give those examples.

I think it is a little bit different:

Fortran and COBOL are implementations of imperative programming languages. The ideas did not change. You still use while, for, if constructs in your ruby/python/java code.

only another level of abstractions was added now with object principles, you can create your own types, you can use polymorphism

but without an idea of imperative programming you would not be able to use these "new" languages (functional programming left aside)

I think it is the same with science, at least regarding physics - einstein did not proove newton wrong. he just extended his theory so that it would better describe the reality (in this case: what happens when you approach speed of light) but for most calculations newton's formulas are still fine.

I was turned off when I started college why I had to learn so much cs theory, but once you start to understanding the concepts and ideas behind certain things you are (theoretical) able to dissect the latest hype look and understand why things are the way they are.

tl;dr: learn concepts and ideas not specific implementations

Perhaps if more programmers took some time to learn the history of the field before jumping in and trying to leave their mark, we'd have more progress and less reinventing of the wheel.

"In computer science, we stand on each others' feet." -- Brian Reid

I think some domains allow themselves to be modeled with enough accuracy that an abstraction allows for the useful observation of data.

I would suspect that domains that can be usefully modeled with a constrained set of abstractions will more practical to work with given the way scientific knowledge is growing.

I hear biologists, neuroscientists talk quite often about the complexity of their domain. If these domains can not be simplified, it may mean that research becomes prohibitively expensive verse other research problems that can be simplified.

I would say that these domains cannot be simplified because we lack the tools. Imagine trying to describe Quantum Mechanics without complex numbers. That's essentially what biologists are stuck with today. Luckily, graph theory is making fast progress and should eventually give us the tools we need...

Leaving aside increases in human longevity, I think things will just continue to get more specialised. From being able to innovate in "everything" in Da Vinci's time, to just science, to just physics, to one branch of physics ...