An awesome explanation. I guess then, for machine learning to succeed in natural language interpretation, we ought to have another layer of abstraction that le

An awesome explanation. I guess then, for machine learning to succeed in natural language interpretation, we ought to have another layer of abstraction that learns meanings from contexts and the general situation. Extending on your idea, I think, not only the verbal context, but physical location / time of day / historical context / current events / social group etc all add their own flavor to the variety of meanings on single word can have. For example, if I am walking in a park and there is a strong gust of wind, I might say "whoa, that's a strong one". The listener, if physically present along with the speaker, will immediately get it. Adding language context is one challenge. Adding the context of environment is a completely different challange.