Humans - for the moment - still drastically lead machines in the realm of creative output.<p>But I love the author's idea of the machine as a "collaborator"<p>Tools like the Source Filmmaker are a good analog. (1)<p>That platform packaged together powerful animation tools along with free assets and a simple UX to empower largely non-technical creators to make awesome CGI clips, movies, and memes.<p>The function of the software referenced in the OP is simple - it colors your photos, it helps to extend or add texture and depth.<p>But you can see the potential for a future where damn near anything you can think of, and draw in stick figure form could come to life as a fully realized image, painting, animation, or experience.<p>(1) <a href="https://en.wikipedia.org/wiki/Source_Filmmaker" rel="nofollow">https://en.wikipedia.org/wiki/Source_Filmmaker</a>
Even in this relatively crude form, I already see a wide range of application for fashion design. For instance, you could take some of the textured flower designs the author presents and place them on graphic tees that could easily sell at Uniqlo, or H&M - and in bulk, these new designs would cost much less than paying multiple designers to create them.<p>(Though as the author points out, you would still need at least 1 designer to train the machine)
Neat article, but I'm skeptical of machine learning to make art easier.<p>I spent some time using PaintsChainer, a ML tool to autofill colors based on a few starting choices and the results were... rough.<p>The problem with ML art, is that it lacks the ability to polish. 90% of the impact of art is in the the last 10% of work, where the artist meticulously refines the piece to turn it from a loosely colored sketch, into a cohesive and complete picture.<p>Many of the tricks and tools to create this polish are heuristics that are not quantifiable or teachable via image sets. They come from an understanding of the "Gestalt" of a picture, or what the "gestalt" should be and then doing the necessary steps to get it there.<p>The other problem is you can't teach an ML algorithm about the hidden volumes in art. Much of drawing/painting is about tricking the eye into perceiving volume when none exists. An ML algorithm can perceive volume and identity after color/light have been applied, because those categories carry data. However, an ML algorithm can't infer what color/light a circle should have to give it the correct volume/perspective. A circle can be a doorknob, a pie, a ball, an eye. Each needing different data applied to it, which the ML algorithm doesn't (and won't ever) have.<p>Personally, I'd welcome a tool to make painting easier. It'd be amazing. However, I don't think we are all that close to a machine creating polished artwork.