I'm not a linguistics expert, but from my vantage point as a computer scientist, it would be hard to conclude that. Chomsky essentially founded the field of formal language theory, and was the first to propose many widely-used constructs, such as the context-free grammar. They may or may not explain how English works, but pretty much everything on the Chomsky hierarchy has been put to productive use in computers.

In a much more general sense, his arguments against blank-slate learning are pretty mainstream in machine learning as well. Chomsky's specific approaches aren't too widely used (though there is some work on grammar induction), but the idea that inductive bias is key to machine learning is widespread, though perhaps in a weaker sense than the very specific and strong inductive bias Chomsky proposes for language learning.

Chomsky did great work for CS (and NLP) through formal language theory, but his work in linguistics -- in particular, his anti-empiricism and hostility to statistical views of language -- was very unhelpful for getting computers to understand language. See e.g. http://www.cis.upenn.edu/~pereira/papers/rsoc.pdf

Put more simply, I'm saying that he did valuable scientific work in developing his formal analysis of grammars, even if it fails to capture human language. He intended it to capture human grammar, but it's an interesting computation-amenable model of grammar even if it isn't how humans speak, because it is pretty much how we now write programming languages. When computer scientists today talk about "grammars", for example someone saying that they're writing "an ANTLR grammar for Clojure", they mean it in the Chomskian sense.

Or are you saying that Freud, despite being entirely wrong, got things moving and eventually helped folks to start taking psychology as a serious science? Or another point I am not understanding?

Yes looking back Freud's theories are bogus. However, that is only looking back and taking in consideration what has happened after. However, whatever happened after in the theory of how mind and human psychology was very much influenced by Freud. A lot of it was a reaction but it was still a reaction to something. Those that came after studied Freud and it was their background.

To say it another way, it hard to predict what would have happened if Freud was not there. We could have been in a worse shape maybe today.

I think that his general linguistic theories might share the same fate as Freud's theories: large, complex theories for which there is simply not much empirical support, making it difficult for people to continue to work on them without their originator imbuing them with his authority.

Though you have to wonder how much of that is a lack of interest in research. PCRE has been shown to not fit his hierarchy, they are more than regular but less than context-free.

It certainly makes me doubt language (the technology of it) as purely cultural and learned.

So we can say first and formost that language is influenced by genetics. Having a tongue, vocal chords, hands, ears. These all help to acquire language.

So now the question is to what degree does genetics influence language and language acquisition?

Is it fully governed by genetics? We know that removing a child from the company of others during development eliminates complex language and severely stunts their ability to acquire language. So, in a single individual, language has never been observed to arise spontaneously and the facility to acquire language is something you can lose.

If you damage certain portions of the brain aspects of language can be removed. See Fluent Aphasia for a particularly odd example. However, these aphasias are known to remit and studies show that other portions of the brain have taken over from the damaged portion. So we know that the capacity for language is not entirely localized to a genetically determined location.

We also know there was a time when there wasn't language and now there is, so at least once in human history language did arise spontaneously. The article you mention is evidence that language can evolve spontaneously in groups of humans. In fact there is evidence that the rudiments of language arise in groups of many animal species including apes, whales, and dolphins. Many animals communicate, but with insufficient sophistication as to be described as a language.

So it is safe to say that there are genetic factors in producing communicative behavior. Sounds, postures, marking etc. I feel it is also safe to say that there are genetic factors that predispose groups of some species to develop more complex systems of communication (but these are not limited to humans) and that groups of humans are particularly good at complex communication.

Unfortunately for Chomsky there is little to no evidence that any one type of language is more likely than another. Grammars, phonemes, words, abstract concepts ... all of these have huge between language variation. The similarities between languages are well explained by either physical characteristics of speech production or regularities in the environment of the languages origin.

So yes, language is both learned, and cultural, even if it isn't purely so.

Consider a different example: constructing artificial vision. Human vision is the result of evolution, of course. It is incredibly inefficient, but evolution is blind (sorry). When we now construct computer vision, we capture an optical image and transmit it optically as long as possible, since this preserves information. The human eye does not: it sends information via neurons to the visual cortex, compromising immensely in bandwidth. That's why we need visual error-correction mechanisms in the brain, and redundancy of visual information (achieved by the eye moving rapidly many times per second, for example).

When we construct optical computer vision that achieves the a similar thing as human vision, the two have nothing in common. You can't plug in the artificial front end into the biological back end. The two systems produce literally different images, that are not comparable. The systems will not communicate with each other. We have skipped the legacy of biological evolution completely. To create an artificial system that accurately corresponds to the biological would be an immense waste.

The same goes for intelligence. Our 'wet' evolved intelligence is an entirely different picture from the project of Strong AI. They will produce very different manifestations of interacting with the world. Why would the latter ever result in the illusion of free will or the self-referentiality of personal identity, two things we assume are parts of human-level intelligence? They are like the neurological channels for conveying an optical image: hopelessly inefficient, but the ones that make sense in the light of our evolutionary legacy.

As a side note, yes we know of a time when there was no language, but it not a good representation to consider that a binary switch. We know of complex communication between other animals, and given that even current languages are in rapid flux, I think it is is fair to think of the transition from pre-language to language a continuum. Language is still arising.

That said his point about the brain being wired to take the less computationally intensive route is a very important insight which I think extends beyond genetics and throughout the evolution of all biological processes.

I don't have his books on me, so I may misconvey this point, but IIRC, he also mentioned regular languages (you know, like with regexes) as another example of a computationally "easier" language family we don't pick up with our language organ. We don't speak arbitrary languages. The space of languages is filtered by genetics.

He delves into this point more deeply in many places and addresses the points you raise with a precision beyond what's found in interviews.

I suppose you could turn it around - assuming our language processing is optimal (bit of a leap) you can infer things about our hardware architecture by the languages which we parse efficiently.

Sadly enough for (U.S. foreign policy and its supporters) he actually supports his claims very well with sources and footnotes, and if you read through this works, you might find that perhaps there is a reason others (who don't just watch Fox News) don't agree with said policy, and also that somehow Americans in certain parts of the world are not "hated because of our freedoms". There are other reasons.

There's a reason no one outside the campus left takes Chomsky's political opinions seriously, and it's not a massive conspiracy.

But the idea that Chomsky's political "contributions" somehow dwarf his contributions to science? That seriously floors me.

— Noam Chomsky

I think it would be nice to have some more references or more explanation before declaring Chomsky detrimental to science.

But he dominated. Where was Norvig or IBM's Watson back then?

Unfortunately things like these don't happen in a vacuum. You can only say looking back that this is didn't or that was a bad idea. You should have said that at that time when the theory was advanced or came up with a better one.

We didn't exactly live in in a totalitarian regime where say some Politburo dictated what the official theory about Genetics should be and everyone else gets sacked, and now finally we have freedom from oppression and we can get back on track.

Ironically that is something that people allude to with regards to Chomsky (ironic because of his anarchist political beliefs); cf. the book the Linguistics Wars. The bottom line is that his theories were very dominant, not because of overwhelming empirical support, but because of his authority.

I don't think that requires that we view language solely as a probabilistic map between sounds and objects. All sorts of emergent behavior appear to be "magical" at first glance.

From what it sounds like, you are just dismissing compelling philosophical issues because it frustrates your beliefs.

As an epiphenomenon of neural firings? Or is saying that 'not allowed' somehow?

The interesting point is the expression power of your model. : to take an example I am somehow familiar with, current large vocabulary speech recognizers have millions of parameters. They work relatively well, but they are very difficult to interpret, and it is hard to see how they help us understanding how speech recognition actually works in our brain.

To make a somehow flawed analogy, every Turing complete language is equivalent, but getting the machine code of a very large project is not very interesting if you want to understand it, while it is mostly enough if you just want to use it.

In general he does. In this article he doesn't talk about he talks about approaches to AI.

> there have been some very good baby studies that show babies inherently know statistics needed to learn probabilistic associations.

Very good. Can we identify how that works and then build a robot that has the same mechanism in a more efficient way than simply simulating a brain at a molecular level. That is his argument here.

> They didn't understand neurons and biology and genetics so well then, so yay, magic things are possible!

So where were you 4-5 decades ago when he proposed his theory to propose a better one?

Why is a real observation from your senses more privileged inside your brain that a random well-formed value by a (hypothetical) random number generator neuron?

As to your second question, I don't see how it relates to my argument, but I'll answer anyway. If you're comparing a observation to a random number, you're looking at the observation qua value, in which case it has the same status. If, however, you look at the level of interpretation (what it means in your brain), the observation has a complex set of relations with the rest of your brain and gives rise to a perception, wheres the random number value is just noise that has to be tolerated by the brain.

Yes you can get some things to work and some to work well but the idea is that perhaps there is a better model that describes the mechanism or the encoding of meaning. That's what Chomsky is trying to say in this particular article. Some stopping at a brute force approach is a fine engineering approach but that doesn't mean everyone should, it is still worth trying to find a better model for it, if at least, just to gain an understanding.

Oh, so, linguistics is just magic then?