Artificial minds have yet to replace human capabilities, but robotics has grown stronger and shows stunning performance. AI software development lets us get irreplaceable helpers that take our tedious work and ensure our business flows without stumbling on obstacles. AI “works” in many businesses and segments: from retailing to medicine and from education to art.
Yet, let us narrow the attention to one of the most elaborate and symbolic activities humanity has always been fond of — art. How does AI create masterpieces? Can AI draw for you? Can it organize visual elements to combine colors and explain your world vision based on your instructions? Here comes the answer.
The Functioning Principle: How AI Draws a Picture
First and foremost, AI learns from what humans have already painted, digitized, and posted. The technology that lets AI “draw” is called Generative Adversarial Network or GAN. Ian Goodfellow proposed the algorithm in 2014. He also introduced the term “adversarial training” to refer to scenarios where two agents are trying to outwit each other. The very first application of GAN was to generate realistic photos. The network consists of two deep neural networks:
- The Generator creates new data instances, trying to fool the Discriminator.
- The Discriminator evaluates data instances and labels them as real (coming from the training dataset) or fake (coming from the Generator).
The idea behind GAN is that the Generator learns from the mistakes it makes and eventually creates data that is indistinguishable from the real data. In other words, the Generator is trying to create data that is as close to the real data as possible. The Discriminator, in turn, is trying to find the differences between real and fake data.
Types of AI Art Creation
There are three primary types of AI art:
- Neural Style Transfer
- Image Inpainting
- Generative Adversarial Networks (GANs)
Neural style transfer
Alt: Neural style transfer example. Picture source: Fast.ai forums
Neural style transfer is a technique that combines the content of one image with the style of another to create a new, third image. The content is the underlying structures in an image, such as the shapes and colors of objects. Style includes arranging those structures, such as the brushstrokes in a painting.
Image inpainting
Alt: Image inpainting example. Picture source: Towards Data Science
Image inpainting is a technique for filling in missing or damaged parts of an image. It can remove objects, such as power lines or buildings, from an image. It can also minimize or hide damage, such as scratches or cracks. Resembles photoshop. Yet, smarter and faster (and might not require any manual operations or shenanigans).
Generative Adversarial Networks (GANs)
Alt: GAN-generated portraits. Picture source: Medium
GANs are artificial intelligence that generates new data instances, such as images or videos. We have already presented and explained its operating principle above.
Another classification exists based on how the human interacts with an AI to create a piece. Here we may choose:
Text instruction creation
Alt: Text-to-Image programs exist. Some of them are free. You type what you want, and the system creates an image for you
That is when an AI requires text instruction to “comprehend” what a user wants to get. The user writes their instruction, chooses style and maybe some filters, and the AI creates several pictures that depict what the user has asked. For instance, the user decides they need a picture of a Kazakh woman. We have tried that, and here is our result (with a charcoal filter):
Alt: An AI-generated portrait created for a test (for free)
Manual creation with AI
Alt: Many free programs let users create pictures with AI. Here is a test haunted ruin (it was free)!
Users have a panel, and they can toggle to adjust an image. As a rule, this method combines neural style transfer, as users must get at least two images to work with the system. There must be a basic picture (the initial image altered by an AI) and an already altered version. While users can reset all settings and get the initial material, they may still use their original or uploaded images to create new pieces.
AI for Art: Use Cases for You
How can you use that artificial digital painter? Does all that have any value except for the aesthetical one? Here come several ideas!
AI art for business
Yes, it’s already used for business purposes. For instance, Unsplash is a popular website where people can find free stock photos. It cooperates with AI artists who generate new material for the site. The artists get revenue from each download (or they may receive a one-time fee).
Another example is EditThisCookie, a Google Chrome extension. The company behind it used GAN to create new icons for the product and generated around 50k variations. That helped them get featured on ProductHunt, which led to an increase in installs and, as a result, more customers.
Moreover, AI-generated visuals can be the #1 option for business website design. Such tools let you create unique, high-quality pieces representing your brand and bringing in the WOW effect. With a bit of editing and maybe even animation, such images will make Google show your website in the first place.
AI art for house decoration creation
We can perceive this idea as part of business, but AI creates products you sell here. That might be an amazing additional service by home designers, architects, and real estate agents. How does it work?
Suppose you go to a meeting with an architect who designs your future home. You enter the office and see a digital painting on the wall that shows what your house will look like after it’s built. The architect says they’ve used AI to create that piece using your requirements and ideas. Of course, you can change anything you don’t like or add new things immediately.
That would be a WOW moment for potential customers!
AI art for exhibitions and museums
Some people believe that AI-generated art should never be exhibited in museums as it’s not “true” art. However, we disagree. The result of using AI for art creation is an amazing piece that may express new feelings, provoke certain emotions, and make people think about the boundaries between human and machine creativity.
Besides, it’s not like AI will replace artists any time soon. The truth is that both humans and machines have their pros and cons when it comes to art. So it would be great to see an exhibition that shows what each of them is capable of!
Some museums already use AI-generated art. For instance, in 2018, the Harvard Art Museums exhibited a portrait created by an AI. The image was based on 15,000 portraits from the 16th to 20th centuries. That was the first time an AI-generated piece was shown in any Ivy League institution.
Final Words
As you can see, many ways to use AI for art exist. And we believe this field will only keep developing in the future! After all, it’s not just about creating new pieces or improving existing ones. It’s also about how AI-generated art can help businesses and people in their everyday lives.
