My experience of AI/NLP research and development is limited compared to HPC and data analysis. During my summer intern as R&D in NLP/query understanding, I studied linguistics and implemented the stoke and order into Chinese NLP, based on its nature as a logographic system. Time will reveal that even though computer programming is highly based on English, AI/NLP may work better in a different language system, which will have the elegant simplicity of mathematics and abundant expressiveness in Chinese.
Back to practice, I have been studying word embeddings like word2vec, and then utilize them for specific linguistic tasks like NER/POS with supervised LSTM + CRF model, or unsupervised models for topic modeling, text summary, etc. From my bilingual background, I realize that knowledge base could be further validated or expanded by multi-languages. I am also interested in the similarity between state-space model and neural network model. Despite the magic non-linearity from activation function, both are seeking optimal/equilibrium with updating techniques based on gradient (and chain rule). The advance of AI/NLP is thus mainly the result of Higher Performance Computing. Leapfrog, MCMC, gradient descent, SGD, batch updating or so are still some compromise regarding to high dimensional parameter space iteration, given the ever-increasing computation power.
Nevertheless, data quality is very important at the moment and it's the cornerstone for commercial products, depending on/deciding the use case and market focus.
These are my 2 cents about AI/NLP. I also have some thoughts on data acquisition and business model waiting to develop.