Nowadays, artificial intelligence seems almost equal to machine learning, especially deep learning. Some have said that deep learning will replace human experts, traditionally very important for feature engineering, in this field. It is said that two breakthroughs underpinned the rise of deep learning: on one hand, neuroscience, and neuroplasticity in particular, tells us that like the human brain, which is highly plastic, artificial networks can be utilized to model almost all functions; on the other hand, the increase in computational power, in particular the introduction of GPU and FPGA, has boosted algorithmic intelligence in a magnificent way, and has been making the models created decades ago immensely powerful and versatile. I'll add that the big data (mostly labeled data) accumulated over the past years is also relevant.
Such developments bring computer vision (and voice recognition) into a new era, but in natural language processing and expert systems, the situation hasn't seemed to have changed very much.
Achieving common sense for the neural networks seems a tall order, but most sentences, conversations and short texts contain inferences that should be drawn from the background world knowledge. Thus knowledge graphing is of great importance to artificial intelligence. Neural networks can be harnessed in building knowledge bases but it seems that neural network models have difficulty utilizing these constructed knowledge bases.
My questions are:
Is a knowledge base (for instance, a "knowledge graph", as coined by Google) a promising branch in AI? If so, in what ways KB can empower machine learning? How can we incorporate discrete latent variables into NLU and NLG?
For survival in an age dominated by DL, where is the direction for the knowledge base (or the umbrella term symbolic approach)? Is Wolfram-like z dynamic knowledge base the new direction? Or any new directions?
Am I missing something fundamental, or some ideas that address these issues?