I think I agree with you. Chomsky is astoundingly intelligent and (I think) I understand what he is getting at (i.e. develop a theory of how something happens and use it to find evidence that supports or refutes that theory, rather than using statistical methods to develop a predictive model that doesn't teach you anything about the underlying system), but you can't but feel a sense of "yeah, but how?"
You can hardly blame the AI researchers for sticking with methods that have been very successful (at least practically speaking) after they had their funding cut out from under them in the 90's for the perceived "failures of AI".
I do like the idea of developing a theory (e.g. vision is processed via algorithm in the brain represented by X) and then attempting to find evidence of that. It can help to avoid the reductionist idea that you need to model everything down to the cellular level in order to understand the brain. It's like trying to disassemble code in memory and then read the algorithm rather than examining every 1 and 0 in memory and trying to make heads or tails of them.
I am not well acquainted with Noam Chomsky and his work, but the dichotomy you refer to I'd like to call "design vs evolution".
If the extreme "design" part the spectrum is what Chomsky talks about: "develop a theory of how something happens and use it to find evidence that supports or refutes that theory", then the extreme end of the "evolution" part of the spectrum must be this:
Choose a set of rules, use genetic programming techniques to rearrange those rules. Evidence you require will be acquired and utilised by the algorithm itself. The only crux is the set of rules to choose from and implementation of the technique. IMO, the more complicated the rules and the more they interact with each other, the better.
...only then can you get results like this: "...Five individual logic cells were functionally disconnected from the rest– with no pathways that would allow them to influence the output– yet when the researcher disabled any one of them the chip lost its ability to discriminate the tones."
See also the story of the MIT magic switch that toggled between Magic and More Magic, required to make a certain machine function, with no wires behind the switch.
You can hardly blame the AI researchers for sticking with methods that have been very successful (at least practically speaking) after they had their funding cut out from under them in the 90's for the perceived "failures of AI".
I do like the idea of developing a theory (e.g. vision is processed via algorithm in the brain represented by X) and then attempting to find evidence of that. It can help to avoid the reductionist idea that you need to model everything down to the cellular level in order to understand the brain. It's like trying to disassemble code in memory and then read the algorithm rather than examining every 1 and 0 in memory and trying to make heads or tails of them.
That's enough rambling from me..