Can Machine Learning Create Art?

We have all heard the saying that “necessity is the mother of invention.” But, what about creativity–where does that come from? And can machines be creative, too? In this article, we will explore the concept of machine learning and its ability to create art. We will also discuss the potential pros and cons.

What Is Machine Learning and What Are Its Applications?

Machine learning is part of artificial intelligence (AI), which designs and develops algorithms that can learn from and make predictions on data. Implementations such as the DALL-E AI image generator prove that we’re nearing a point where machines will be able to create art as good as that of humans. 

These algorithms can automatically improve when given more information. In other words, machine learning enables computers to improve tasks without being explicitly programmed to do so.

Some applications of machine learning include:

  • Translating languages
  • Speech recognition
  • Predicting financial markets
  • Recognising objects in images or videos–for example, Google Photos uses machine learning to automatically tag your photos with labels like “sunset” or “beach.” Another project by Google called DeepDream was also designed to recognise and classify images. We dive into this in our recent post on how Google DeepDream works.

How Can Machine Learning Be Used to Create Art?

Machine learning can be used to create art in several ways. For example, it can be used to generate new images or videos, often done by using a Generative Adversarial Network (GAN). A GAN consists of two components: a generator and a discriminator–the generator generates new data that is similar to the training data, while the discriminator tries to distinguish between the training data and the generated data. The two networks compete, and as they do so, they each get better at their respective tasks. This results in the generator being able to create increasingly realistic images or videos.

Another way to create art is by transforming existing images or videos. For example, a neural network can be trained to colourise black and white images. This is often done by using a Convolutional Neural Network (CNN), which is a type of neural network particularly well-suited for image recognition tasks.

What Are the Benefits of Using Machine Learning to Create Art?

There are two important benefits–firstly, machine learning algorithms can be used to create images and videos that humans cannot manually create. Secondly, machine learning can be used to create art that is personalised to the viewer. For example, a neural network can be trained to generate images that are similar to those that the viewer has liked in the past. This is known as “style transfer.” The style of specific painters from the past can be brought back using style transfer; neural networks can be trained to generate images in the style of Van Gogh or Picasso. This means you could draw a portrait of your dog using machine learning, and it will appear as though it was made by Picasso himself!

Are There Limits to Using Machine Learning to Create Art?

Unfortunately, there are a few potential disadvantages. Machine learning algorithms can be difficult to control because the algorithm often explores a large range of possible solutions, and it is not always clear why it has chosen a particular solution.

Machine learning algorithms often require a lot of data to be effective, which can be difficult to obtain, especially if you want to create art in a style not well represented in existing data sets. It may also not be suitable for use on devices with limited power, such as smartphones.

Still, this does not mean you cannot use machine learning to create art. Our AI text-to-image generator uses neural networks to convert text prompts into digital paintings. You can also check out our post on whether or not AI is a form of art if you are interested in learning more about AI-generated art.