What is AI Generator?

Overview

Generative AI is a kind of artificial intelligence technology that relies on deep learning models trained on large data sets to create new content. Generative AI models, which are used to generate new data, stand in contrast to discriminative AI models, which are used to sort data based on differences. People today are using generative AI applications to produce writing, pictures, code, and more. Common use cases for generative AI include chatbots, image creation and editing, software code assistance, and scientific research.

People are putting generative AI to use in professional settings to quickly visualize creative ideas and efficiently handle boring and time-consuming tasks. In emerging areas such as medical research and product design, generative AI holds the promise of helping professionals do their jobs better and significantly improving lives. AI also introduces new risks which users should understand and work to mitigate.

Some of the well-known generative AI apps to emerge in recent years include ChatGPT and DALL-E from OpenAI, GitHub CoPilot, Microsoft’s Bing Chat, Google’s Bard, Midjourney, Stable Diffusion, and Adobe Firefly. Many other organizations are experimenting with their own generative AI systems to automate routine tasks and improve efficiency.

What are AI art generators?

AI art generators are programs designed to automatically generate unique images. These apps use sample images or even a text prompt to create new and unique artwork. By tokenizing that art and turning it into an NFT, artists are able to sell it and grant full ownership to the buyer. AI art generators are simple to use, and in most cases require a few easy steps. The AI algorithm does most of the work, meaning all the artist needs to do is to come up with an idea.

The concept of AI art is based on the notion that machine learning algorithms can produce unique, original images. Similar to painters who spend years perfecting their skills, AI can learn to generate original images through intense training. They are trained to analyze large amounts of image data using General Adversarial Networks (GANs). The best NFT creator apps simply require users to enter a few keywords or phrases, then the NFT art generator handles the rest.

How does generative AI work?

If you’ve enjoyed a surprisingly coherent conversation with ChatGPT, or watched Midjourney render a realistic picture from a description you just made up, you know generative AI can feel like magic. What makes this sorcery possible?

Beneath the AI apps you use, deep learning models are recreating patterns they’ve learned from a vast amount of training data. Then they work within human-constructed parameters to make something new based on what they’ve learned.

Deep learning models do not store a copy of their training data, but rather an encoded version of it, with similar data points arranged close together. This representation can then be decoded to construct new, original data with similar characteristics.

Building a custom generative AI app requires a model, as well as adjustments such as human-supervised fine-tuning or a layer of data specific to a use case.

Most of today’s popular generative AI apps respond to user prompts. Describe what you want in natural language and the app returns whatever you asked for—like magic.

What are some use cases for generative AI?

Generative AI’s breakthroughs in writing and images have captured news headlines and people’s imaginations. Here are a few of the early use cases for this rapidly advancing technology.

Image generation. Generative AI image tools can synthesize high-quality pictures in response to prompts for countless subjects and styles. Some AI tools, such as Generative Fill in Adobe Photoshop, can add new elements to existing works. This is the core tools of U Infinity in making the UNfts platform.

Writing. Even before ChatGPT captured headlines (and began writing its own), generative AI systems were good at mimicking human writing. Language translation tools were among the first use cases for generative AI models. Current generative AI tools can respond to prompts for high-quality content creation on practically any topic. These tools can also adapt their writing to different lengths and various writing styles.

Speech and music generation. Using written text and sample audio of a person’s voice, AI vocal tools can create narration or singing that mimic the sounds of real humans. Other tools can create artificial music from prompts or samples.

Video generation. New services are experimenting with various generative AI techniques to create motion graphics. For example, some are able to match audio to a still image and make a subject’s mouth and facial expression appear to talk.

Code generation and completion. Some generative AI tools can take a written prompt and output computer code on request to assist software developers.

Data augmentation. Generative AI can create a large amount of synthetic data when using real data is impossible or not preferable. For example, synthetic data can be useful if you want to train a model to understand healthcare data without including any personally identifiable information. It can also be used to stretch a small or incomplete data set into a larger set of synthetic data for training or testing purposes.