Generative Adversarial Neural Networks: Infinite Monkeys and The Great British Bake Off


If you had an infinite number of monkeys typing at keyboards, could you produce Shakespeare? But how you would know once they'd typed Shakespeare? In this example, monkeys are what are called Generators in AI, and the English student who checks their work to see if they have written Shakespeare (or anything good) is called a Discriminator. These are the two components of an Generative Adversarial Neural Network. Adversarial Neural Networks are oddly named since they actually cooperate to make things.

GAN with Keras: Application to Image Deblurring – Sicara's blog


We extract losses at two levels, at the end of the generator and at the end of the full model. The first one is a perceptual loss computed directly on the generator's outputs. This first loss ensures the GAN model is oriented towards a deblurring task. It compares the outputs of the first convolutions of VGG. The second loss is the Wasserstein loss performed on the outputs of the whole model.

6 Times AI Tried to Get Creative, and How the Results Turned Out


Breakthroughs in neural networks--a type of machine learning that vaguely imitates the structure of neurons in the brain--have given rise to a form of the technology called generative AI that can do everything from imitate photorealistic images and abstract art to composing music or writing. While these tools have raised concerns over their potential use for fabricated news footage and circumventing copyright laws, the vast majority of content produced by this type of AI still has a slightly off-kilter quality that betrays its non-human creator. As the cultural debate around AI-fueled art begins to heat up, we're looking back on what kind of work has actually come out of the initial experiments in this space. Here are six examples of AI's use in creative processes that offer a sense of the current state of the technology and a hint at its larger potential: Google's DeepDream computer vision software, first released in 2015, turns any image into an abstract hallucinogenic version of itself by finding and enhancing certain patterns within the image. While the system might have little practical use for creative professionals on its face, it represented an early foray into the type of AI-generated art that has come to proliferate the open-source community.

It's Getting Hard to Tell If a Painting Was Made by a Computer or a Human


Cultural pundits can close the book on 2017: The biggest artistic achievement of the year has already taken place. It didn't happen in a paint-splattered studio on the outskirts of Beijing, Singapore, or Berlin. It didn't happen at the Venice Biennale. It happened in New Brunswick, New Jersey, just off Exit 9 on the Turnpike. Nobody would mistake this place as an incubator for fine art.