It is funny how every AI post on HN turns into a speculative discussion forum full of words "I think", "likely", "I suspect", "My guess" etc, when all the research is available for free and everyone is free to download and read it to get a real understanding of what's going on in the field.
>what I see is the fact that networks have huge capacity to fit to data and are deep (rely on a hierarchy of features).
Actually recurrent neural networks like LSTM are turing-complete, i.e. for every halting algorithm it is trivial to implement an RNN that computes it. It is non-trivial to learn these parameters from algorithm IO data, but for many tasks it is possible too.
>I suspect that most of what is being learned (and solved) is perception of useful features from the raw game images.
It is not this simple, deep enough convnets can represent computations, the consensus is that middle and upper layers of convnets represent some useful computation steps. Also note that human brain can only do so much computation steps to answer questions when in dialogue, due to time and speed limits.
>My guess is we may need a fundamental breakthrough in a newfangled hierarchical learning system that is better suited for language to “solve” NLP.
This is being worked on, see the first link for Memory Networks and Stack RNNs, DeQue RNNs, Tree RNNs. Deep learning is a very generic term, there are dozens of various feedforward and recurrent architectures that are fully differentiable. The full potential of such models has not been nearly reached yet and maybe language understanding will be solved in the coming years (again, the first link shows that it is in process of being solved).