What's funny is I feel in some ways the exact opposite is true.
When Google creates a program to learn Go, it learns go so well that it knows it (arguably) knows it far better than any human (even if it isn't flawless).
But what did we learn about go? Well, we learned a bit about the opening I guess, since Lee Sedol has become fond of the "AlphaGo opening," but other than that... not much, right?
That's the funny thing about neural networks. They can converge to a set of weights that, when activated, perform better than any human. But we can't look at Weight 483 and Weight 958 and say "Ah, that's where it decided the corner is very valuable!" or something.
It learns, we don't. We can only learn from what it can then show us it has learned.
This is not true. We can look at networks and manually look at their weights to determine what features it learned, even high level features. You can do it right now (by examining a pretrained network).
People don't bother to because:
1) it's a very boring problem (we already have a high-level view of what networks learn through various visualizations, and what you'd learn would be specific to one network learned for one dataset)
and 2) it's very tedious and not repeatable (have to do it all over for each new dataset and each new model).
We might be thinking of different scopes of machine learning.
You could look at AlphaGo's weights for the entire neural network for ten thousand years. But you would become no better at Go. The only way AlphaGo can help us improve at Go is by showing us what it has learned in the games it plays.
You're dancing around, and haven't really defined, what it means to "learn Go."
Sure, humans wouldn't become better at Go. But that's a limitation of the human brain (we're not good at mathematical memorization and computation).
For all we know, what the network has learned about Go (a highly complex and interconnected set of statistical dependencies) is what there is to learn about Go. You're implicitly making the assumption that what the network learns about Go is guaranteed to be translatable to something humans can learn.
On the contrary, what the network learns is merely reducible, with loss of accuracy to what humans can understand. And that is an active area of research (feature visualizations and explanations), but that is tangential to your point.
The exact opposite seems to be true. In many domains, machine learning showed even very simple algorithms easily outperform "experts". It's not uncommon to find that experts perform barely better than chance. See: http://lesswrong.com/lw/3gv/statistical_prediction_rules_out...
