Yeah, I feel that. I tell myself they're only tools, really no stranger than time <-> frequency domain transformations to someone unfamiliar with the fourier transform.

Yet there really is something disturbing about seeing a computer resurrect so much of the mind of an artist who's been dead for nearly a century. I know the GPU doesn't understand what it's doing, but did Monet?

When I paint I don't really understand. There may be occasional moments of clarity, I like to think that I make deliberate choices based on the emotions and thoughts within me that I'd like to reflect to the people experiencing my art, but... Honestly am I so different from a neural network?

I am a neural network. Much of what goes into creating a piece of art is based on intuition, on experience and practice, pathways eroded into my mind from years of thoughts traversing the same landscape. The art I create is unique to me, not in that it cannot be reproduced but in that every action I take is a reflection and an echo of all the moments remembered and forgotten that create me.

How much of Monet's mind is in a GPU in Berkeley?

I think that is only true on first glance. But if you look a little deeper, none of the photo->painting are really accurate. The painters had more than color scheme as information. Van Gough, for example, did not always use a correct perspective. In general, the deeper meaning of art is not in the technique but in the perspective of the artist.

That said, I am amazed by the results even so.

I also want to note here, that I don't see any reason why machine intelligence could not produce meaningful works of art but it will require a new way of looking at it.

Or.. we just greatly overestimated what our brains were doing all along..

>How much of Monet's mind is in a GPU in Berkeley?

Very little, arguably none. Imitation is far removed from creation. If you see an art student reproduce Monet's paintings or redo an existing image in Monet's style, would you ask "how much of Monet's mind is in the art student?"

Are you a graphic artist or a cab/truck driver? :)

I started buying AI textbooks circa 2010 anticipating something was about to change dramatically.

Sadly, I didn't work through the books like I had planned at all. Lacked the discipline to come home and work through them at the time. I really regret it now as things are blowing up and, as you said, it seems like every week there is something new and interesting

Luckily, deep learning is built on super complicated mathematical foundations, but the practice isn't. You could quickly get up to speed with a library like Keras and be productive, building things and trying them out without needing years of theoretical training. I'd highly recommend looking into it, most of the papers are relatively easy to read, and rely largely on empirical results and intuition, rather than deep theoretical proofs or the like.