Artificial intelligence (AI) is the technology that enables machines to perform tasks that normally require human intelligence, such as recognizing objects, generating text, or creating images. AI has been used to create artistic works since the mid-20th century, when computer graphics emerged as a new medium for expression. In this blog post, we will explore the history and evolution of AI-generated graphics, from the debut of AARON, one of the first AI artists, to the recent breakthroughs of Stable Diffusion, Midjourney, and DALL-E, which can create realistic and diverse images from text prompts.
AARON: The Pioneer of AI Art
One of the first and most influential AI art systems is AARON, developed by Harold Cohen, a British painter and professor at the University of California, San Diego. Cohen began working on AARON in 1973, with the goal of creating a program that could autonomously generate original drawings and paintings1. AARON is based on a symbolic rule-based approach, which means that it uses a set of predefined rules and concepts to create images. For example, AARON knows how to draw basic shapes, such as circles, squares, and triangles, and how to combine them to form more complex objects, such as plants, animals, and people. AARON also knows how to use colors, perspectives, and compositions to create aesthetically pleasing images2. AARON's output has evolved over the years, from simple black and white drawings to colorful and abstract paintings. Cohen also developed a way for AARON to paint using special brushes and dyes that were chosen by the program itself without mediation from Cohen3.
AARON is considered one of the first AI artists, and has been exhibited in many museums and galleries around the world. AARON's work has also raised philosophical questions about the nature and origin of creativity, and the role and responsibility of the human artist4.
GANs: The Revolution of AI Art
The next major milestone in the history of AI-generated graphics is the invention of generative adversarial networks (GANs), a new approach to generative models, in 2014 by Ian Goodfellow, then a student at the University of Montreal5. Generative models are algorithms that can learn the properties of a collection of data, such as images, and then create new data that statistically fits in with the original collection. For example, a generative model trained on a collection of images of faces can generate new images of faces that look realistic and diverse6.
GANs are a special type of generative models that involve two neural networks, which are algorithms inspired by the structure and function of the human brain. The two neural networks are called the generator and the discriminator, and they work against each other in a game-like scenario. The generator tries to create fake images that look real, while the discriminator tries to distinguish between real and fake images. The generator learns from the feedback of the discriminator, and the discriminator learns from the data. The process continues until the generator can fool the discriminator, and the discriminator can no longer tell the difference.
GANs have been used to create stunning and realistic images of various objects, such as faces, animals, landscapes, and artworks. GANs have also been used to manipulate and enhance images, such as changing the style, color, or expression of an image. GANs have also enabled new forms of artistic expression, such as creating images from text, sketches, or sounds.
GANs have revolutionized the field of AI art, and have sparked a wave of innovation and experimentation among artists and researchers. GANs have also raised ethical and social issues, such as the potential for misuse, deception, and plagiarism.
Stable Diffusion, Midjourney, and DALL-E: The Future of AI Art
The most recent and exciting developments in the field of AI-generated graphics are the emergence of new techniques that can create images from text prompts, such as Stable Diffusion, Midjourney, and DALL-E. These techniques use a combination of generative models and natural language processing, which is the branch of AI that deals with understanding and generating natural language, such as text or speech.
Stable Diffusion is a technique developed by researchers at OpenAI, a research organization dedicated to creating and promoting beneficial AI. Stable Diffusion can generate high-quality and diverse images from text prompts, such as “a cat wearing a hat” or “a painting of a city in the style of Kandinsky”. Stable Diffusion uses a diffusion model, which is a type of generative model that can create images by gradually adding
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