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Computer-generated art will have to face very soon the problem that the sheer novelty that a computer can generate (at least apparently) meaningful content like

Computer-generated art will have to face very soon the problem that the sheer novelty that a computer can generate (at least apparently) meaningful content like photos, etc. will run out, and people will start expecting that the results are actually good.

pjc50 8y ago
That problem is easily solved by producing enough machine-generated content to drown out all actual human creativity, so people will come to expect and demand that sheen of bland meaninglessness. (For a less dramatic version of this consider the role of autotune)
chongli 8y ago
As someone who has played roguelikes for nearly 2 decades now, I'm not holding my breath. For a niche genre with a small developer community, roguelikes punch well above their weight in novelty, complexity, and replay value. Computer generated art isn't really about computers, it's about what the human artist can achieve by tuning the parameters. Like any other medium, there is an enormous range to operate within. At the same time, there are suitable constraints which tend to enhance (rather than limit) creativity.
- thanatropism 8y ago
  > Computer generated art isn't really about computers, it's about what the human artist can achieve by tuning the parameters That's not "computer generated art", that's "art generated with computers". I understand that there's a lot of nontrivial tuning to current generative NN models (I spent the downtime between christmas and new years learning to train image-making GANs in several simultaneous DigitalOcean droplets; the major issue for me as a non-expert was balancing the expressive power of networks and the complexity of training them in a short span of time). But the novelty in the results of a GAN isn't "whoa, a computer enabled me to make this surreal photo-like bitmap", but "whoa the computer learned how to draw photorealistic noses". This is partly due to an anthropomorphization that we indulge even for relatively dumb programs, but also due to the fact that when we mess with hyperparameters we're not learning a correspondence with (aesthetic) results, we're at best learning a correspondence with network performance, training time, discriminator vs. generator learning curves, etc. This will be a poor analogy because I'm typing in a hurry, but a painter of fine representational art learns a precise relation between brush movement (something that he does with muscles) and brush stroke (a result); but the aesthetic result is the brush stroke itself. He eventually learns to think in brush strokes; he exists in very close relation to the canvas while making brush movements. What we're looking at with computer-generated art is a situation where (1) what is sought is a surprise, not a system-and-method for obtaining finely-chosen aesthetic results from expertly-wielded tools and (2) we seek it by becoming masters at the thing that produces the surprise -- we learn to think in terms of online optimization first and then with the higher order abstractions of dense and convolutional layers and training regimens. We're not making paintings, we're making brushes. And in the beginning it's awesome that paintings are even possible. But if they fail to continue to attract and capture attention, well, brush-making becomes a money-loser.
  - chongli 8y ago
    That's not "computer generated art", that's "art generated with computers". I don't think such a distinction exists. whoa the computer learned how to draw photorealistic noses It didn't, though. You just fed it a whole bunch of photos of people's noses and it learned how to combine them in a way that was pleasing to you, after you spent a long time tuning the parameters and adjusting the algorithm. I fail to see how this is any different from tuning a roguelike's dungeon generating algorithm to produce natural caves and other structures that are pleasing to you. Heck, lots of roguelikes already include datasets of pre-made dungeon pieces (sometimes called vaults) that they can snap together like Lego. This is exactly analogous to brushes in your claim. The only real difference between a roguelike's dungeon generator and a neural network is that the developer has a better understanding of what's going on so that it doesn't always seem like magic. Having said that, roguelikes still tend to surprise their developers every day.

pjc50 8y ago

That problem is easily solved by producing enough machine-generated content to drown out all actual human creativity, so people will come to expect and demand that sheen of bland meaninglessness. (For a less dramatic version of this consider the role of autotune)

chongli 8y ago

As someone who has played roguelikes for nearly 2 decades now, I'm not holding my breath. For a niche genre with a small developer community, roguelikes punch well above their weight in novelty, complexity, and replay value. Computer generated art isn't really about computers, it's about what the human artist can achieve by tuning the parameters. Like any other medium, there is an enormous range to operate within. At the same time, there are suitable constraints which tend to enhance (rather than limit) creativity.

thanatropism 8y ago
> Computer generated art isn't really about computers, it's about what the human artist can achieve by tuning the parameters That's not "computer generated art", that's "art generated with computers". I understand that there's a lot of nontrivial tuning to current generative NN models (I spent the downtime between christmas and new years learning to train image-making GANs in several simultaneous DigitalOcean droplets; the major issue for me as a non-expert was balancing the expressive power of networks and the complexity of training them in a short span of time). But the novelty in the results of a GAN isn't "whoa, a computer enabled me to make this surreal photo-like bitmap", but "whoa the computer learned how to draw photorealistic noses". This is partly due to an anthropomorphization that we indulge even for relatively dumb programs, but also due to the fact that when we mess with hyperparameters we're not learning a correspondence with (aesthetic) results, we're at best learning a correspondence with network performance, training time, discriminator vs. generator learning curves, etc. This will be a poor analogy because I'm typing in a hurry, but a painter of fine representational art learns a precise relation between brush movement (something that he does with muscles) and brush stroke (a result); but the aesthetic result is the brush stroke itself. He eventually learns to think in brush strokes; he exists in very close relation to the canvas while making brush movements. What we're looking at with computer-generated art is a situation where (1) what is sought is a surprise, not a system-and-method for obtaining finely-chosen aesthetic results from expertly-wielded tools and (2) we seek it by becoming masters at the thing that produces the surprise -- we learn to think in terms of online optimization first and then with the higher order abstractions of dense and convolutional layers and training regimens. We're not making paintings, we're making brushes. And in the beginning it's awesome that paintings are even possible. But if they fail to continue to attract and capture attention, well, brush-making becomes a money-loser.
- chongli 8y ago
  That's not "computer generated art", that's "art generated with computers". I don't think such a distinction exists. whoa the computer learned how to draw photorealistic noses It didn't, though. You just fed it a whole bunch of photos of people's noses and it learned how to combine them in a way that was pleasing to you, after you spent a long time tuning the parameters and adjusting the algorithm. I fail to see how this is any different from tuning a roguelike's dungeon generating algorithm to produce natural caves and other structures that are pleasing to you. Heck, lots of roguelikes already include datasets of pre-made dungeon pieces (sometimes called vaults) that they can snap together like Lego. This is exactly analogous to brushes in your claim. The only real difference between a roguelike's dungeon generator and a neural network is that the developer has a better understanding of what's going on so that it doesn't always seem like magic. Having said that, roguelikes still tend to surprise their developers every day.