Intellectually understanding things is useful. There's no anti-intellectualism here.
It's become increasingly clear that Chomsky's approach to language is not going to generalize to AI successes or explanations of many domains of human performance, as was promised. That approach has not yielded fruitfully anywhere except the narrow realm of syntax (in particular typologies of syntax). While machine learning has kicked butt left and right.
There is no reason people can't go on studying syntax at the same time machine learning expands. But it would be dishonest to ignore the extreme and exclusive claims laid down by Chomsky's school at the outset of the cognitive revolution. These claims were given a very liberal benefit of the doubt for decades. A lot of good work has been done in syntax. But now those claims about AI and psychology-in-general are clearly threadbare, since they have not yielded the promised fruits either in AI or in explaining human functions. They have not even yielded plausible IOUs. Remember, this was supposed to explain pretty much everything. Not even language acquisition has been explained.
That is empirical inadequacy. Who cares if Chomsky thinks it's beautiful?
And it's equally clear that learning of various kinds has to occur and also gives us the more parsimonious and elegant solutions to problems (no-solution and no-explanation is not elegant even if it is simpler).
Machine learning has a great deal of conceptual beauty - if you study it rather than pooh-poohing it because of some facile abductive argument to UG.
Computing power isn't a problem. The scale of the human brain is enormous and we are still nowhere near reaching it with our computational resources.
What about research showing things like intermediate traces exist, we parse sentences in accordance with binding principles, we don't postulate gaps for fillers in sentences in island contexts unless there'll later be a potential filler gap? All of these are facts we know from abstract work in Chomskyan syntax, and yet they've been replicated in laboratories. So, your claim that there's no fruits is just simply empirically false
It's become increasingly clear that Chomsky's approach to language is not going to generalize to AI successes or explanations of many domains of human performance, as was promised. That approach has not yielded fruitfully anywhere except the narrow realm of syntax (in particular typologies of syntax). While machine learning has kicked butt left and right.
There is no reason people can't go on studying syntax at the same time machine learning expands. But it would be dishonest to ignore the extreme and exclusive claims laid down by Chomsky's school at the outset of the cognitive revolution. These claims were given a very liberal benefit of the doubt for decades. A lot of good work has been done in syntax. But now those claims about AI and psychology-in-general are clearly threadbare, since they have not yielded the promised fruits either in AI or in explaining human functions. They have not even yielded plausible IOUs. Remember, this was supposed to explain pretty much everything. Not even language acquisition has been explained.
That is empirical inadequacy. Who cares if Chomsky thinks it's beautiful?
And it's equally clear that learning of various kinds has to occur and also gives us the more parsimonious and elegant solutions to problems (no-solution and no-explanation is not elegant even if it is simpler).
Machine learning has a great deal of conceptual beauty - if you study it rather than pooh-poohing it because of some facile abductive argument to UG.
Computing power isn't a problem. The scale of the human brain is enormous and we are still nowhere near reaching it with our computational resources.