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Artificial intelligence art is any visual artwork created through the use of an artificial intelligence (AI) program. Artists began to create artificial intelligence art in the mid to late 20th century, when the discipline was founded. Throughout its history, artificial intelligence art has raised many philosophical concerns related to the human mind, artificial beings, and what can be considered art in a human–AI collaboration. Since the 20th century, AI art has been exhibited in prominent museums and has won prestigious awards. The increased availability of AI art tools to the general public in the 2020s AI boom provided opportunities for creating AI art outside of academia and professional artists. Commentary about AI art in the 2020s has often focused on issues related to copyright, deception, defamation, and its impact on more traditional artists, including technological unemployment. History Early history The concept of automated art dates back at least to the automata of ancient Greek civilization, where inventors such as Daedalus and Hero of Alexandria were described as having designed machines capable of writing text, generating sounds, and playing music. The tradition of creative automatons has been throughout history, such as Maillardet's automaton, created in the early 1800s. The academic discipline of artificial intelligence was founded at a research workshop at Dartmouth College in 1956, and has experienced several waves of advancement and optimism in the decades since. Since its founding, researchers in the field have raised philosophical and ethical arguments about the nature of the human mind and the consequences of creating artificial beings with human-like intelligence; these issues have previously been explored by myth, fiction and philosophy since antiquity. 1950s to 2000s: Early implementations Since the founding of AI in the 1950s, artists and researchers have used artificial intelligence to create artistic works. These works were sometimes referred to as algorithmic art, computer art, digital art, or new media. One of the first significant AI art systems was AARON, developed by Harold Cohen beginning in the late 1960s at the University of California at San Diego. AARON uses a symbolic rule-based approach to generate technical images in the era of GOFAI programming. Cohen developed AARON with the goal of being able to code the act of drawing. In its primitive form, AARON created simple black-and-white drawings. Cohen would later finish the drawings by painting them. Throughout the years, he also began to develop a way for AARON to also paint. Cohen designed AARON to paint using special brushes and dyes that were chosen by the program itself without mediation from Cohen. AARON was exhibited in 1972 at the Los Angeles County Museum of Art. In both 1991 and 1992, Karl Sims won the Golden Nica award at Prix Ars Electronica for his 3D AI animated videos using artificial evolution. In 2001, Scott Draves won the Fundacion Telefonica Life 4.0 prize for Electric Sheep, which used AI to create an infinite animation by learning from its audience. In 2009, Eric Millikin won the Pulitzer Prize along with several other awards for his artificial intelligence art that was critical of government corruption in Detroit and resulted in the city's mayor being sent to jail. 2010s: Deep learning In 2014, Ian Goodfellow and colleagues at Université de Montréal developed the generative adversarial network (GAN), a type of deep neural network capable of learning to mimic the statistical distribution of input data such as images. The GAN uses a "generator" to create new images and a "discriminator" to decide which created images are considered successful. Unlike previous algorithmic art which followed hand-coded rules, generative adversarial networks could learn a specific aesthetic by analyzing a dataset of example images. In 2015, a team at Google released DeepDream, a program that uses a convolutional neural network to find and enhance patterns in images via algorithmic pareidolia. The process creates deliberately over-processed images with a dream-like appearance reminiscent of a psychedelic experience. In 2018, an auction sale of artificial intelligence art was held at Christie's Auction House in New York where the AI artwork Edmond de Belamy (a pun on Goodfellow's name) sold for $432,500, which was almost 45 times higher than its estimate of $7,000–10,000. The artwork was created by "Obvious", a Paris-based collective. The website Artbreeder, launched in 2018, uses the models StyleGAN and BigGAN to allow users to generate and modify images such as faces, landscapes, and paintings. In 2019, Stephanie Dinkins won the Creative Capital award for her creation of an evolving artificial intelligence based on the "interests and culture(s) of people of color." Also in 2019, Sougwen Chung won the Lumen Prize for her performances with a robotic arm that uses AI to attempt to draw in a manner similar to Chung. 2020s: Transformer models In the 2020s, text-to-image models, which generate images based on prompts, became a trend. In 2021, using the generative pre-trained transformer models used in GPT-2 and GPT-3, OpenAI released a series of images created with the text-to-image AI model DALL-E. Later in 2021, EleutherAI released the open source VQGAN-CLIP based on OpenAI's CLIP model. In 2022, Midjourney was released, followed by Google Brain's Imagen and Parti, which were announced in May 2022, Microsoft's NUWA-Infinity, and the source-available Stable Diffusion, which was released in August 2022. DALL-E 2, a successor to DALL-E, was beta-tested and released. Stability AI has a Stable Diffusion web interface called DreamStudio, plugins for Krita, Photoshop, Blender, and GIMP, and the Automatic1111 web-based open source user interface. Stable Diffusion's main pre-trained model is shared on the Hugging Face Hub. Tools and processes Imagery There are many tools available to the artist when working with diffusion models. They can define both positive and negative prompts, but they are also afforded a choice in using (or omitting the use of) VAEs, LorAs, hypernetworks, ipadapter, and embeddings/textual inversions. Variables, including CFG, seed, steps, sampler, scheduler, denoise, upscaler, and encoder, are sometimes available for adjustment. Additional influence can be exerted during pre-inference by means of noise manipulation while traditional post-processing techniques are frequently used post-inference. Artists can also train their own models. In addition, procedural "rule-based" generation of images using mathematical patterns, algorithms that simulate brush strokes and other painted effects, and deep learning algorithms such as generative adversarial networks (GANs) and transformers have been developed. Several companies have released apps and websites that allow one to forego all mentioned options entirely while solely focusing on the positive prompt..... Discover the Tessa Creative Art popular books. Find the top 100 most popular Tessa Creative Art books.