This sounds ambitious. I wonder if they can also address the following problem. Currently, deep learning toolkits need thousands of training images to classify images of, e.g., dogs and cats. A human, in contrast, could learn the difference between a dog and a cat by looking just at a single example (or perhaps a few). So right now, deep learning is too much "simple" pattern matching, and too little real "AI".
Anyone who had raised a child would tell you that, a human, requires years of training to learn the difference between a dog and a cat, by looking countless examples.
Of course, the disparity between deep neural nets and human brains remains unknown. A human learns the difference between a cat and a dog, while at the same time, learns so many different things, yet a neural net only learns the difference between a cat and a dog. We don't know how much we don't know.