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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

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 bunch of images of white flowers. But if it can synthesize the idea properly, one should be able to generate a flower of a variety of descriptions. Yellow flower with blue center. Red flower with yellow center. Blue flower with black edges and black center. etc.. From the way they describe the functionality it should be able to do these things so in a way I don't doubt it, but I want to see how it performs on phrases that induce combinations of ideas that are well outside of the training set yet refer to individual ideas within the training set.

taneq 10y ago
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.
- jcannell 10y ago
  >How do you "synthesize ideas" if not by combining parts of your own personal database of images/concepts? 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).

taneq 10y ago

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.

jcannell 10y ago
>How do you "synthesize ideas" if not by combining parts of your own personal database of images/concepts? 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).