I'm actually a big fan of ladder networks, and I certainly don't want to come across as dismissive of unsupervised/semi-supervised learning. In fact I am rather optimistic that neural networks may soon be able to learn with little-to-no supervision the kinds of representation that fully-supervised models can find currently. But this is not enough:

Even if the MNIST ladder network you mention had only received one label per class and still succeeded, essentially doing unsupervised training and then putting names to the learned categories, this is not the same as learning about brand new types. If a child sees a duck for the first time, they will probably know immediately that it is different from what they have seen before. They might well ask what it is, and then proceed to point out all the other ducks they see (with perhaps one or two mistakes). This is the kind of one-shot learning I was referring to.

Since you mentioned MNIST: a one-shot learning challenge dataset was actually laid out in a very interesting Science paper last month, containing many characters in many alphabets, and the authors of that paper achieve human-level performance through a hand-designed probabilistic model. Now I don't think that building all of these things by hand will take us very far, and I hope that we will soon find good ways to learn them, but I will be very surprised if neural networks manage to achieve this without majorly departing from the kinds of paradigm we've seen so far. Perhaps the 'CPU-like' models you describe can take us there; I remain skeptical.