I agree totally that a "non-pure" deep learning approach is going to be necessary in pushing the field forward. World shattering results from new deep architect

I agree totally that a "non-pure" deep learning approach is going to be necessary in pushing the field forward. World shattering results from new deep architectures like AlexNet are becoming more and more rare and improvements are becoming incremental. Language models, for example, showed some impressive results in recent years, but we're still a long long way from generating realistic and useful text. I'm not sure if throwing more data at larger architectures will get us there. Maybe, maybe not. The work by Tenenbaum at MIT is a particularly interesting mix of psychology, computational statistics, and neuroscience. It really challenges the commonly repeated notion that neural networks are completely unrelated to actual brains.