I think the idea is interesting but I'm not convinced it really "synthesizes ideas" so much as treats the neural network like a database of images that it mixes. Now, I could be wrong, but because of the way the results are presented it doesn't tell me that it's any good at picking up the meaning of the phrase. The results show a single phrase and a set of images it generates. White flower with yellow center, and a b…
How do you "synthesize ideas" if not by combining parts of your own personal database of images/concepts? Even in your example of "X flower with Y center [and Z features]" you start with a mental picture of a flower you've seen (or a generalisation from many flowers you've seen) and then modify it with your mental picture of colours X and Y and features Z.
Procedural generation can be far more complex than just linear blending, which is all that a shallow net can do. For example, consider the full generative process which creates a frame from the game No Man's Sky. It is enormously more complex than a simple shallow net that can just do linear blends of previous examples - many many nonlinear processing steps to go from a small random seed to intermediate databases for terrain and objects and finally down to pixels.
If you look at the actual net design used here, it's only a few layers deep, and not very big. Much much closer to 'linear blending' than what our brains do (which is presumably vaguely closer to what no man's sky does).