I think about the AI language problem a lot while raising my kids. The article notes the word "forever" and how an AI must distinguish the literal from the figurative meaning of the word in context. My five-year-old still doesn't grasp the literal meaning of this word as "never-ending." To him, "forever" is simply a very very long time. He has the same problem with the concept of "infinity," where the word means both "the biggest number" but also has the characteristic (in his mind) of having an upper quantifiable bound. His young mind has not yet recognized the paradox that "infinity" is the biggest number, so what does it mean when I say, "Infinity plus one"?

Neural networks are going to make huge inroads to the AI language problem simply by exposing the AI to example after example of words in varying contexts. But I wonder if the real problem is getting those neural networks to let go of unnecessary data? Humans rely on excited neurons to recognize patterns, but our neurons let a lot of sensory input pass us by to keep from getting bogged down in the details. Are the image-recognition AI's described in the article capable of selective attention? Will they get bogged down in the morass of information in trying to pattern-match every word to every image and context they learn?