In previous writing I’ve described what has arguably become the most widely cited theory of generative art. Based on notions from complexity science, and in particular Murray Gell-Mann and Seth Lloyd’s notion of “effective complexity,” I argue that generative art is not a subset of computer art. Rather, generative art turns on the use of autonomous systems and the artist ceding control to those systems. As part of this theory for generative art, I’ve introduced a series of problems. These are not problems in the sense that they require single correct solutions. Rather they are questions that the artist will consider when making a piece; that critics and historians will typically address in their analysis; and that insightful audience members will ponder. They are problems that typically offer multiple opportunities and possibilities. It is notable that, for the most part, these problems equally apply to both digital and non-digital generative art; to generative art past, present, and (it is believed) future; and to ordered, disordered, and complex generative art. In addition, these same problems or questions are generally trivial, irrelevant, or nonsensical when asked in the context of non-generative art. In a sense the applicability of these questions can cleanly divide art into generative art and non-generative art. More importantly, the exploration of these questions can illuminate the analysis and critique of generative art. More recently a new form of neural-network-based artificial intelligence called “deep learning” has appeared on the scene. Deep learning has been applied to digital art creation. In this paper I explore whether the problems in generative art noted above hold up well in this new artificial intelligence context for generative art. The conclusion reached is that our current complexity-based theory of generative art can easily assimilate the use of deep learning.