Also, there is strong evidence that prediction is the main (if not the only) function of the brain. I would recommend Hohwy's book (https://books.google.fr/books/about/The_Predictive_Mind.html...) for an explanation of this idea.
Sure it does.
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
> Hawkins' mantra is sequence based memory.
Note that Hawkins wrote “On intelligence” in 2005.
Schmidhuber wrote “Learning complex extended sequences using the principle of history compression” in 1992, and “Long short-term memory”, with Hochreiter, in 1997.
Today, a LSTM-based RNN can answer your Gmail.
http://people.idsia.ch/~juergen/rnn.html
http://googleresearch.blogspot.com/2015/11/computer-respond-...
Why not? If the model was trained on a large enough corpus of texts, it will have seen lots of answers to your question, or similar questions. It can, in principle, extract the important features of those answers, and present them to you as an answer. This is pretty much how the majority of humans would answer "the most meaningful" questions.
I am the working example. I can read texts and answer questions. HTM is an attempt to explain how I do that. Obviously, it's an incomplete theory, but its main ideas make sense to me. You got a better theory?
I think that predicting the next word in a sentence does actually get to the point of the issue, which is why the Hutter Prize [0] is defined the way it is.
The human brain does: (1) operate sequentially on words (2) keeps an internal state that contains semantic information about what's been heard/seen (3) learns the semantic representation (4) is excellent at predicting the next word in a sentence, and continually does so (5) it's difficult to argue it's not learning to predict the next word in a sentence (ie optimised to that task).
Looking at that list, I'd say the only differences between what the brain is doing and what algorithms like these (in general) are doing are just the particular algorithms for learning the semantic representations in working memory and performing prediction, and less importantly the form of the input (humans have more cues available, including taking actions to get more information)
(Admittedly parsing a complicated sentence can require jumping backwards, but spoken language will have a very limited nesting level, and this just means including a small piece of the input in the working memory.)
In the last week I've actually been reading about algorithms for this (though I didn't know NuPIC was applied to sentences, thanks very much for mentioning it). Aside from the range of RNN-based systems that mietek mentioned, here are a couple more recent papers, related to word2vec, on representing the semantic and syntactic content of a sentence as vectors: [1], [2]. Both are based on optimising for prediction of the next words in a sequence. Now I agree it's dubious to try to reduce a sentence to a small fixed length representation, whether that's e.g. 2400 real numbers in [2], or a pattern of neuron activations in a RNN, but from the experimental results (which admittedly aren't always so great) they seems to be encoding something meaningful, though far too much information gets thrown away.
[1] Quoc V. Le, Tomas Mikolov, 2014, Distributed Representations of Sentences and Documents, http://arxiv.org/abs/1405.4053
[2] Ryan Kiros et al, 2015, Skip-Thought Vectors, http://arxiv.org/abs/1506.06726
Edit: added a bit more about context