""neural algorithms that generalize well, the way that the human cortical algorithm generalizes well"."
I think we have already been seeing this for years with image recognition, speech recognition, and other pattern recognition problems. As with those problems, playing Go is one of those things you can easily get heaps of data for and formulate it as a nice supervised learning task. The task is still spotting patterns on raw data with learned features.
However, the current deep learning methods don't (seem to) generalize well to all that our brains do - most of all learning to do many different things with online small input of data. I have not seen any research into large scale heterogenous unsupervised or semi-supervised learning with small batches of input - these big neural nets are still used within larger engineered systems to accomplish single specific tasks that require tons of data and computing power. Plus, the approach here still uses Monte Carlo Search in a way that is fairly specific to game playing - not general reasoning.
Clearly this is another demonstration Deep Learning can be used to accomplish some very hard AI tasks. But I don't think this result merits thinking the current approaches will scale to 'real' AI (though perhaps a simple variation or extension will).