Designed by AI

Artwork Created by A.I.

Artwork created by artificial intelligence is currently just a niche industry, with a handful of artists using A.I. to create work and a handful of galleries exhibiting it. But in the past few years the industry has been growing, with new artists and new galleries emerging. Despite the fact that AI art is still in its infancy, it has already begun to have a significant impact on the art world. In October 2018, a piece of AI art sold at Christie’s for $432,500, making it the most expensive work of AI art ever sold at auction.

AI art, also known as machine learning art or generative art, is art created by algorithms or artificial intelligence. These artworks can be created by training algorithms on existing data sets, such as images or photographs, or by giving algorithms creative freedom to generate new art from scratch. AI art is often created with the help of neural networks, which are algorithms that are designed to mimic the workings of the human brain. Neural networks can be trained to recognize patterns and to generate new data that conforms to those patterns. For example, a neural network could be trained on a dataset of images of cats and then asked to generate a new image of a cat.

Ultimately, it is up to the viewer to decide if AI-created art is "real" art. Whether or not you believe AI-created art is “real” art, there is no doubt that it is becoming increasingly realistic and may one day be indistinguishable from art created by humans.

Some of the most well-known A.I. artists include Mario Klingemann, Robbie Barrat, and Memo Akten. Klingemann is a German artist who has been creating A.I. art since the early 2000s. Barrat is an American artist who creates A.I. art using a program called Sketch-RNN. Akten is a Turkish artist who uses a variety of A.I. programs to create his art. The first A.I. art gallery was opened in London in 2017 by art dealer, Alexia Goethe. The gallery, called the Artificial Intelligence Art Gallery, was the first of its kind in the world.

In 2018, the first A.I. art fair was held in New York City. The fair, called the Art and Artificial Intelligence Fair, featured art from over 30 A.I. artists. Since then, A.I. art galleries have opened in other major cities around the world, including Paris, Berlin, and Tokyo. And A.I. art fairs have been held in London, San Francisco, and Amsterdam.

The growth of the A.I. art industry has been fueled by the increasing availability of A.I. tools and the decreasing cost of computing power. As A.I. tools become more accessible and cheaper, more people are able to create A.I. art. Artificial intelligence has also been used to create artworks in a variety of other mediums, including poetry, film, and video games. The future of the A.I. art industry is unknown. But as A.I. technology continues to develop, it is likely that the industry will continue to grow.

Artwork created by artificial intelligence can take on many different forms, depending on the specific algorithms and training data used. Some examples of AI-created artwork include images generated by deep learning algorithms, 3D models created by generative adversarial networks, and paintings created by reinforcement learning agents.

The Artificial Design platform utilizes a variety of techniques to generate images:
Generative Adversarial Networks (GANs): GANs are a type of AI algorithm that can generate new data that is similar to training data. This can be used to create new images, or to generate new versions of existing images.

Deep learning: Deep learning algorithms can be used to create new images from scratch, or to modify existing images. For example, a deep learning algorithm could be used to generate a painting in the style of a specific artist.

Evolutionary algorithms: Evolutionary algorithms are a type of AI that can create new images by starting with a population of random images and then selecting the images that are most fit (based on some criterion) to survive and reproduce. This process can be repeated to generate new generations of images, which can eventually converge on images that are similar to the training data.