Chomsky did the opposite of what you're saying. He didn't ignore spoken language. He said that human vocalization is independent of language, and that the way our brains can manipulate and use sound (a cognitive capability, not specifically an aural one) is the fundamental differentiator that allows us to make compound ideas, and our specific use of language is a byproduct.

Example: a programming language's capability to produce complex software does not come from some inherent quality of language. It comes from binary. 0's and 1's, representing basic logic, and that being built on top of with an abstract "tool" called a language. If the binary logic didn't work, the language wouldn't do anything.

A dolphin can make sounds, and technically has a language, but they can't manipulate or recursively compound concepts (as far as we can tell) in order to create modified ideas. If they could, they probably would have come up with vastly more advanced fishing methods than the (admittedly novel) ones they have now.

It is different, but there may be some universal principles that are relevant more abstractly among both cases. Of particular interest is the empirical notion that statistical models of a certain form will always tend to "average out noise" and "learn meaningful patterns" up to the capacity that those models have for representing said patterns. A parallel notion to this is the hypothesis dubbed "thermodynamic origins of life". The universal principle binding these two seemingly disparate topics is one that seems to underlie any sense of "learning" in physical systems: that semantics of those systems depend on their representational power, and the semantics they do come to represent are the results of adding up many pushes in one "direction" (phase space / state space / etc.) encoding a pattern, and adding up many random noise jiggles will cancel out but give you a first-order sense of variance of those semantic features as expressed by the environment.

As this description is so overly abstract, an exercise for the reader is to try to work through an explanation of how, say, a river delta comes to "learn" about its environment by "reacting" to the influences at its borders, and how it "encodes" whatever it is that it learns in the substrate that it inhabits.

Almost everything in ML is like that. It seems so obvious in hindsight. It's maybe what I love most.

Residual connections are so simple, so obvious and so vital. Yet nobody came up with them until 2015?

Yes, protozoans don't have brains and yet they exhibit complex behavior.