There is a lot of controversy surrounding art and — in general — content created by Artificial Intelligence. Can the content be considered art if it is made by a computer? Is the AI generated content better than a human-made one? There are a lot of questions and disagreements but there are definitely some types of content — for example, journalistic news articles or illustrations and diagrams — that can be created automatically without employing human content creators. That’s when AI can help.
What is Generative AI?
Generative AI is a technology that can create new content by utilizing existing text prompts, images, videos, audio files and so on. With the Generative AI technology, computers can detect the underlying pattern related to the input and produce similar content. Generative AI already does a lot. It produces text and images, blog posts, program codes, poetry, artwork and even wins fine-arts competitions.
There are different Generative AI techniques. For example:
Generative adversarial networks (GANs):
GANs are two neural networks: a generator and a discriminator that pit against each other to find equilibrium between the two networks:
The generator network generates new data or content that resembles the source data
The discriminator network differentiates between the source and the generated data in order to recognize what is more similar to the original data
Transformers:
For example, GPT-3, LaMDA, and Wu-Dao. Those transformers imitate cognitive attention and differentially measure the significance of the input data parts. They are trained to understand the language and images, learn classification tasks and generate texts and images from massive datasets
Variational auto-encoders:
Auto-encoders encode the input into compressed code while the decoder reproduces the initial information from this code. If chosen and trained correctly, this compressed representation stores the input data distribution in a much smaller dimensional representation.
How can Generative AI be used?
The Generative AI technology can be used for:
Generating photographs of human faces, objects and scenes
Generative AI can produce real looking photographs using, for example, the GAN models.
If you want to learn more about the GAN models — especially the StyleGAN-NADA model — you can read my article about it!
Image-to-image conversion
Generative AI can translate one image to another (for example, black and white photos to color photos; day photos to night photos; real photographs to artistic paintings in the famous artists’ styles and so on).
Text-to-image Generation
It produces realistic photographs from text prompts of simple objects like birds and flowers and other basic descriptions using, for example, the DALL·E 2 or the StackGAN systems.
If you want to learn more about the DALL·E 2 model, you can check out my article about it!
Film Restoration
It restores and improves old images and old movies by upscaling them to 4K and beyond. It can generate 60 frames per second instead of 23 or less, eliminate the noise and add color, for example, with the help of Topaz Gigapixel AI.
You can watch an example of the AI Film Restoration here!
Semantic Segmentation
Semantic-image-to-photo translation converts input that are semantic images or sketches to photo realistic images or photographs.
Face Frontal View Generation
It generates front-on photos from photos taken in different angles for a face verification or face identification system.
Photos to Emojis Transformation
It changes real photos to emojis or cartoonish faces. This technology is used in Machine Learning filters on social media like Snapchat, Instagram or TikTok.
If you want to learn more about Machine Learning filters, you can read my article about it!
Face Aging
It generates older versions of faces from photos. This technology is also used in Face Modification entertaining apps.
Media and entertainment
There are different ways to apply Deep fake and other generative AI technologies. For example, deep fake technology can help with localization of content (e.g. dubbing of a movie). By using face synthesis and voice cloning such as the CRISPR technology, the actors’ original voices can be matched with a lip-sync.
Benefits of Generative AI
Enhanced Identity Protection
Generative AI that can create avatars that can conceal the real appearance of people who do not want or are not comfortable disclosing their identities for any reason while being interviewed or working online.
Improved Quality of Output
Generative AI systems can help to get high-quality images, video, audio and other content even if the original input content is distorted and far from perfect.
Decreased Financial and Reputational Risks
Generative AI tools can quickly detect malicious or at least suspicious activities and prevent all kinds of damage to a business or a creator.
Challenges of Generative AI
Security
Generative AI can be used for scamming people.
Overestimation of Capabilities
Enormous amounts of training data is needed for the Generative AI algorithms to perform tasks. But even with that, GANs cannot create entirely new images or texts but only combine the visual and semantic information they already learned.
Unexpected Outcomes
It’s not easy to control the behavior of some Generative AI, for example, like GANs. The Generative AI models can perform unstably and generate something unexpected.
Now, when you know basic information about the Generative AI technology, you can use it to benefit your business and content creation in general. Good luck!
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