>> Perhaps semantic negation is hard? More to the point, it doesn't do semantics at all, only syntax, if you will. It has learned correlations between images a

>> Perhaps semantic negation is hard? More to the point, it doesn't do semantics at all, only syntax, if you will. It has learned correlations between images and words but it has no way to map either the words or the images to their meanings. All it knows is that this word appears often in correlation with that image. So when it sees the word, it gives you the image. What is the word "no" correlated with? Probably, absolutely anything. Most likely "no" is as probable to appear as the description of an image with apples as it is to appear as the description of an image without apples. But "apples" is likely to describe an image with apples, even if it has "no" in it. For example "An image of apples in a basket with no handle" has "no" and "apples". So- you get apples, "no" or not. In a sense, nobody has told it that "no" means "no". (Also, it doesn't know that "apples" means "apples". "Apples" is only the label of some sets of pixels in a space. If you scrambled the labels so that "apples" was correlated with images of elephants... "an image with no apples" would get you an image with elephants).