| How can the present state-of-the-art in NLP ML be more than a toy having limited scope if the most interesting, and truly useful real-world applications require impossible amounts of hardware resources, along with algorithmic running times that will take until the heat death of the Universe? Mainly I'm talking about the topics in and related to the famous paper What Computers Can't Do.
http://en.wikipedia.org/wiki/What_Computers_Cant_Do Before I get nitpicked for generalizing NLP/ML to all out "Hard AI", I would like to note that almost all of the most trivial language processing tasks we meatsacks perform a billion times per day in our daily lives involve the semantic interpretation of abstract representations. It would be trivial to sit down and begin enumerating specific types of word and association problems that a child could perform, but which no supercomputer with a 1,000 engineers toiling it on could solve in the general sense. By no means am I saying that everyone in the field should not full court press ahead on the state of the art, as we'll never get there unless we try. I am asking about the extent that real-world NLP/ML use is constrained enough so as to be nearly worthless hokum buzzwords. (i.e. analytics, recommendation engines for pre-conditioned, overtrained and context-free representations, behavioral profiling for ads, statistical models of dynamical systems, etc.) I hope this doesn't come off as being negative, as I'm really more interested in the question of Hard AI and how it relates to computational linguistics problems we can solve today or in the near future without some kind of unforeseen "singularity" level break through in AI. Or is the general state still as bad as when Minksy demolished perceptrons, causing the "nuclear winter decade" in AI? I don't have a PhD, just a pure math undergrad with a high motivation to have kept going at it, and this topic itself is so obscure & difficult that it's not often one has the chance to ask someone who knows, so I am very interested in the perspective of present day researchers. |