Such creative software can be used for autonomous creative tasks, such as inventing mathematical theories, writing poems, painting pictures, and composing music. However, computational creativity studies also enable us to understand human creativity and to produce programs for creative people to use, where the software acts as a creative collaborator rather than a mere tool. Historically, it's been difficult for society to come to terms with machines that purport to be intelligent and even more difficult to admit that they might be creative. For instance, in 1934, some professors at the University of Manchester in the United Kingdom built meccano models that were able to solve some mathematical equations. Groundbreaking for its time, this project was written up in a piece in Meccano Magazine.

As the Associated Press reports, art conservation experts in Santa Fe and Chicago will use this type of technology to detect, track and analyze a particular type of chemical buildup found on many of O'Keeffe's paintings that cause thousands of tiny blisters to form and grow. Canvases used by O'Keeffe and many other 20th century artists were primed with non-drying fats or oils and when they combine with pigments or drying agents, it can lead to a buildup of soap that causes the blisters. "They're a little bit bigger than human hair, and you can see them with the naked eye," Dale Kronkright, an art conservationist at the Georgia O'Keeffe Museum, told the AP. And when enough of those little blisters show up, they can begin to darken a painting. "Left unchecked, they will continue to grow, both grow in number and grow in size -- and in damaging effect," Konkright said.

If you would like to apply a pre-trained model to a collection of input photos (without image pairs), please use --dataset_mode single and --model test options. For example, landscape painting - landscape photographs works much better than portrait painting - landscape photographs. For example, these might be pairs {label map, photo} or {bw image, color image}. A and B should each have their own subfolders train, val, test, etc.

"The most significant arousal-raising properties for aesthetics are novelty, surprisingness, complexity, ambiguity, and puzzlingness," say Elgammal and co. "Novelty refers to the degree a stimulus differs from what an observer has seen/experienced before. "Too little arousal potential is considered boring, and too much activates the aversion system, which results in negative response," say Elgammal and co. That has important implications for the way their generative adversarial network, or agent, is set up. "The agent's goal is to generate art with increased levels of arousal potential in a constrained way without activating the aversion system," they say. Some of the machine-generated images were produced by the creative adversarial network, but others were produced by the generative adversarial network that simply reproduces artistic styles it has learned.

In the art AI, one of these roles is played by a generator network, which creates images. The other is played by a discriminator network, which was trained on 81,500 paintings to tell the difference between images we would class as artworks and those we wouldn't – such as a photo or diagram, say. "You want to have something really creative and striking – but at the same time not go too far and make something that isn't aesthetically pleasing," says team member Ahmed Elgammal at Rutgers University. Once the AI had produced a series of images, members of the public were asked to judge them alongside paintings by people in an online survey, without knowing which were the AI's work.

To train a model on your own datasets, you need to create a data folder with two subdirectories trainA and trainB that contain images from domain A and B. For example, landscape painting - landscape photographs works much better than portrait painting - landscape photographs. A and B should each have their own subfolders train, val, test, etc. In /path/to/data/A/train, put training images in style A.

Here's a sentence you don't get to read everyday: Kristen Stewart has surprised the artificial intelligence community by publishing a paper on machine learning. The Twilight actress recently made her directorial debut with the short film Come Swim, and in it used a machine learning technique known as "style transfer" (where the aesthetics of one image or video is applied to another) to create an impressionistic visual style. Along with special effects engineer Bhautik J Joshi and producer David Shapiro, Stewart has co-authored a paper on this work in the film, publishing it in the popular online repository for non-peer reviewed work, arXiv. Once more: Kristen Stewart of Twilight fame directs movie; writes arXiv paper about using StyleNet during production https://t.co/NZ4I1yhQUN To be someone in Hollywood, you've got to put your ML papers on Arxiv and you better use TensorFlow... https://t.co/2Rcg1ccJ36

Early this year AI system AlphaGo cracked the ancient Chinese game Go, one of the most complex that ever existed. If there is one thing that fuels the speed of AI development, it's data. In 2011, Benjamin Grosser launched his Interactive Robotic Painting Machine, which paints abstract pictures with oil on canvas and responds to the sounds in its environment. That way Google's AI will be able to learn how creative people work, making itself more creative in the process.

A "new" Rembrandt portrait is actually the creation of a 3-D printer -- and a statistical analysis of 346 paintings by the Dutch master. "The Next Rembrandt," as it's been dubbed, was the brainchild of Bas Korsten, creative director at the advertising firm J. Walter Thompson in Amsterdam. Bas Korsten, executive creative director of the J. Walter Thompson Amsterdam agency, stands with the painting at its unveiling Tuesday in Amsterdam. Bas Korsten, executive creative director of the J. Walter Thompson Amsterdam agency, stands with the painting at its unveiling Tuesday in Amsterdam.