Nice post! I’ve also referenced this article in one of my recent posts, https://www.linkedin.com/pulse/solving-intelligence-real-world-problems-jacques-ludik, where I’ve, amongst other things, also mention some focus areas in advancing the state-of-the art in Machine Intelligence (e.g., unsupervised learning, recurrent neural networks, malleable & shapeable knowledge representations, sensory learning using patterns & sequences of patterns in cause-effect way). Even for level 2 “classification with memory” systems which effectively corresponds to recurrent neural networks, there is a clear need for improved supervised and unsupervised training algorithms. Unsupervised learning should for example build a causal understanding of the sensory space with temporal correlations of concurrent and sequential sensory signals. Much research still needs to be done with respect to knowledge representations and integrating this with deep learning recurrent neural network systems all the way from level 3 to level 5 systems (classification with knowledge, imperfect knowledge and collaborative systems with imperfect knowledge). Lots of room for creating substantially more intelligent systems to help solve real-world problems!