Doesn't they say the same thing about photography when it was emerging? ;)
Agreed. This is just depressing and is a detriment to society. If this keeps advancing at its current rate, good art will be so trivial to generate that it won't be special anymore. Art will slowly morph into a banal distraction, with creating an original piece being as easy as applying an Instagram filter. The role of the human artist will change from a craftsperson to someone who picks a bunch of parameters, gives it to the AI, and chooses the best output. This type of technology is a threat to the very existence of art as a craft, will completely devalue artwork, and will make the journey of training to become an artist obsolete. I hate these researchers for what they're doing to a field that I love.
I disagree. There will always be demand for real artists. Like any other digital software, this is just a tool with the possibility to help artists create compelling worlds faster and add realism that would otherwise have taken days to make using other methods. As a 3D character artist, I would love to use this to create quick backdrops to place my characters in to enhance final renders.
Nvidia has recently presented an AI that with an unsupervised learning method for computers which can create mind-blowing fake videos. The system will allow users to set up weather, turn day into night, and change almost anything.
Previous techniques relied on massive amounts of data and has problems with training the machines to find their own patterns. Researched had a hard time dealing with mapping a low-resolution image to a corresponding high-resolution image and colorization (mapping a gray-scale image to a corresponding color image).
Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains. Since there exists an infinite set of joint distributions that can arrive the given marginal distributions, one could infer nothing about the joint distribution from the marginal distributions without additional assumptions. To address the problem, we make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs. We compare the proposed framework with competing approaches and present high quality image translation results on various challenging unsupervised image translation tasks, including street scene image translation, animal image translation, and face image translation. We also apply the proposed framework to domain adaptation and achieve state-of-the-art performance on benchmark datasets. Code and additional results are available in this https URL .
The modern machines can turn sunny days into rainy ones, create the equivalent of a “snow plow” filter for videos, and more.
Reality is now a strange thing thanks to projects of Nvidia. Should we be worried? Let’s discuss!