This is the same argument that was used against artificial neural networks. Neural network of type A can't do X, therefore neural networks will never do Y.

Language is immensely complex, and real human language involves things which are not encoded in text (and i'd remind you that you were trying to infer meaning from text specifically, not the full multi-channel robustness of humans communicating), we don't even have a full handle on what all of the cognitive processes and factors are that go into the production and understanding of language (although we've developed a lot of interesting work to those ends).

So hearing folks give up claim that Chomsky is correct because our current tools aren't up to the job is a bit puzzling, because we don't even have a complete understanding of what sort of thing language is or what sorts of things we are as systems which can use language.

Chomsky has opinions (and some facts) about what language is, and we are, but he does not have solid proof to confirm his specific conjectures. Is human language context free? context sensitive? Something else? (Chomsky's minimalist program uses movement along a tree to preserve referentiality and a bunch of junk, alternative syntactic frameworks such as HPSG uses directed graphs as the basis of their language modeling. Still others do weirder things like higher order combinatoric logics. And unfortunately none of the theoretical frameworks appear to be without their drawbacks)

Just blue-skying here, but it seems to me that if I knew enough about how a statistical program worked, I could craft a sentence that would utterly confuse it, even though it was perfectly intelligible to a normal English speaker. A putative strong-AI program could not be fooled in this way.

Long distance dependencies are an issue in language modeling that do need to be accounted for, but all that tells me is that Markov chains aren't the right structure to model language (unless, maybe you had a MASSIVE amount of data, and a markov chain of an order high enough that you account for the majority of sentences. maybe).

(1) Raising a child. My dad often remarks that he's surprised that my daughter knows how to use a word in just the right context. I'm not, because this is a natural product of mimicry: if you copy what others say, you usually use the words in the correct context. As with computer-generated text the exceptions are often hilarious.

(2) Song lyrics. I had a very clear experience where I just could not understand the refrain of Gold Guns Girls. It sounded completely unintelligible until I read the lyrics. After that, it sounded crystal clear. Why would reading the lyrics make the song sound different? Context.

Did you produce a better chat bot based on UG? - No? Then on what basis are you junking machine learning?

Machine translation is far from a solved problem. But Chomsky's school claimed they were going to solve it in the 60s or perhaps the 70s. Do you know what is the basis for the most successful current approach to machine translation?

Analysis of text. (But not the kind of simplistic junk one does in a Markov chatter)

All you have done is suggest that some beautiful perfect text model exists natively in every person. (Presumably this evolved somehow - or if you are Chomsky, it just developed like a crystal for no apparent reason). This isn't an explanation of anything unless you actually find that model instantiated in the brain. But this is just not happening. So either our instruments are still too crude to detect it, or it's not really there.

Appealing to an as-yet unknown perfect universal text model does not build a better chat bot or a better explanation of human behavior.

True understanding of intelligence must incorporate an understanding of how learning occurs. Because anyone who watches children sees learning occurring, and only doctrinaire Chomskyists deny that it occurs (because it is not beautiful enough and some abductive argument is claimed to show that it is not sufficient).

Where is the data that statistical methods don't 'advance our understanding'? What does an EEG tell us about the brain works?

Fundamentally it is gibberish not for any simple algorithmic reason, but because generation is occurring without any respect to context or meaning beyond what randomly emerges from the graceful juxtaposition of randomly chosen words. It is purely about the combinations of words (in that sense, syntax). This shouldn't be surprising - who ever actually expected that generating a kind of syntax model would result in coherent thoughts? At most it can generate texts like weird dreams, it shouldn't be surprising that the result is not a cogent discussion of current events.

This does not mean that the same information cannot be used in more sophisticated ways. But these wouldn't be a Markov chatbot. The Markov model would effectively be a component in a larger system that needed to use words. It isn't at all clear that the Markov model is the best possible one, but it is just groundless dogma to insist that learning can't have anything to do with real performance.

So it sounds like what you decided was that you wanted to explore one path and not the other. Nothing wrong with that, but it's a very different statement.

The state of the art is improving a bit, although this method still knows nothing of meaning so it can often generate some strange sentences. Still, I wouldn't write off the whole field yet--just because something didnt work with tools years ago doesn't mean it isn't possible.

1.) Measure data, then make an educated guess. 2.) Make an uneducated guess.