While processing natural languages, using machine learning techniques is not any more optional, but more mandatory. Dealing with so many variations and imprecission?? in natural language (especially, these are properly cleaned up by attentive writers or speakers) without utilizing some sorts of machine leanring approaches (recently more with deep learning approaches) sounds near impossible.

However, the terms like "learning" and "understanding" used in "mahcine leanring" and "natural language understanding" often confuse people who do not have a more detailed understanding. When these people are higher-ranked decision makers, this confusion could beget wrong decisions, which often could be quite detrimental.

While in Samsung, I also experienced several similar occassions. In many cases, the people I have interacted with were extremely sharp and intelligent. Thus, I think that the confusion could come from some of the following causes. Especially, such confusion is repeating. I think that it is worthwhile to make writing for future reference.

1. Natural language understanding is not necessarily *understand* natural language. 

I think 

Add another draft: Common confusion on ML-based NLU
Add another draft: Common confusion on ML-based NLU
