Here is the actual paper for those interested http://levan.cs.washington.edu/ngrams/objectNgrams_cvpr14.pd...
Summary of the paper for those who don't want to read it: So basically there are two categories of "learning" involved in this sort of research, supervised and unsupervised. In supervised learning, someone gives the computer a long list of concepts and their attributes ("frog", "green frog", "jumping frog") and a set of pictures to go with each item, and feeds them into a visual-recognition algorithm. In unsupervised…
I'm more interested in metaphor and analogy.
My 3.5 year old son said "look at the rain! It is bouncing like hopping frogs!"
I don't know if he created that. It's not in any of his books. I guess he jumps like a hopping frog at nursery and transferred that to rain.
I'm not so interested in a computer that is trained on frogs, and which sees a hopping frog and describes it as such. If it saw a hopping cat and said this thing is hopping but I don't know what it is, then I'd be interested.
Am I being too harsh on the robots?