The history of AI art started in 1973, when Harold Cohen, a computer scientist and artist, created a program called AARON. AARON was the first known AI program to create artwork. It was groundbreaking at the time because it allowed a machine to generate art without human input. However, the real boom in AI art started many years later, in 2014.
In 2014, a new type of technology called generative adversarial networks (GANs) was developed. Although GANs were not originally made for creating art, they have become a key tool in AI art today.
At first, the generator isn’t very good, so the discriminator easily spots that the image is fake. But as both the generator and discriminator continue working together, they get better. The generator creates more realistic images, and the discriminator becomes more skilled at telling what’s real and fake. Over time, the generator can create images that even the discriminator can’t tell apart from real ones. This process is key to how AI creates art today.
In 2015, researchers took a step by training computers to generate images based on text prompts. For example, if you typed in “a dog driving a car,” the computer would create an image of a Dog in a Car. This common process is called image to text.
The major thing came in 2021 when OpenAI, the creators of ChatGPT, released DALL-E, a text-to-image program. DALL-E was named after the famous artist Salvador Dali and Pixar’s character WALL-E. It was trained on millions of images and concepts, allowing it to generate high quality images from simple text prompts. This marked the start of the text to image the AI art revolution.
In 2022, open-source developers began developing more AI art generators, using all the technology possible. One of the most popular AI art generators today is called MidJourney, Leonardo, and more. However, as these technologies advance, challenges like Nvidia’s AI chips facing heating issues have raised concerns about their efficiency. AI art generators like DALL-E and MidJourney work by analyzing massive datasets of images and their descriptions. They don’t copy images directly from the dataset but use what’s called latent space to create new, unique images.
How Do AI Art Generators Work?
AI art generators depend on deep learning models to create images from text. These models are trained on hundreds of millions of images and text descriptions on the internet. For those looking to stay informed on the latest AI updates, following advancements in AI tools and techniques can provide valuable insights. When the prompt is like “a pink sea ,” the AI starts by looking at the data it was trained on images of seas, and the color pink.
As it processes the information, it keeps improving the result until it gets it right. The more specific the text prompt, the more detailed the AI’s guess will be. For example, more words like “a pink sea full of green dolphins and yellow whales,” the AI will process more details to match the request.
When AI generates an image, it doesn’t see things the way humans do. It doesn’t think it processes pixel values and tiny bits of pink, green, and yellow. But it has learned to create patterns based on those pixels. It places objects like the sea, dolphins and whales in different parts of the map. When it creates an image, it pulls data from these different points to generate a final result.
As AI art is still evolving artists and developers continue to explore new ways to use AI to push the boundaries of art. AI art works by using deep learning models to analyze images and text, then creating new artwork based on patterns it has learned. Programs like DALL-E and MidJourney have made it possible for anyone to create stunning images from just a few words, opening up exciting possibilities for the future of art.
