Generative AI is an artificial intelligence technology that uses deep learning models to create high-quality, realistic content, including text, images, audio, and video. Generative AI analyzes and identifies patterns in large data sets and then uses that information to create new content from the shared characteristics of the original data set, based on prompts from the user.
Generative AI is not necessarily a new technology. Its origin stems from 1960s technology used to power early chatbots. The 2014 discovery of Generative Adversarial Networks (GANs), which leveraged opposing generator and discriminator neural networks, led to the creation of realistic imagery and audio with AI, which was integral in shaping the generative AI models available for mainstream use today.
Transformers, a type of machine learning that can process large sets of unlabeled data and identify connections between words and information, and Large Language Models (LLMs), have further shaped modern generative AI capabilities, and the rapid adoption of accessible and user-friendly platforms such as ChatGPT.
Popular Generative AI Categories
Generative AI is gaining momentum as all types of organizations discover the benefits and use cases of using this technology to create and scale content efforts. The following are some of the most popular generative AI categories.
- AI Image Generators: An AI image generator uses an Artificial Neural Network (ANN) modeled after the human brain to process information provided in a text prompt and create an image based on the details and description from the prompt. While this technology has existed for decades, the recent advancements in text-to-image technology make it possible to create high-quality, unique images in the simple input-output that makes generative AI so accessible for creative image generation.
- AI Video Generators: AI video generators leverage machine learning technology to analyze large datasets, including text, videos, and images. Machine learning algorithms use this data to identify objects and emotions to create relevant, high-quality video content. Most AI video generators use a three-step process, including analysis, storyboarding, and editing, to give users tools to try various imagery and storylines to create unique, compelling video content.
- AI Voice Generators: AI voice generators use deep learning algorithms, Text-to-Speech (TTS) technology, and Natural Language Processing (NLP) to convert written text into audio that is unique and nuanced for forms of speech, dialects, and emotions. The audio can be customized by gender, language, dialects, and accents to create diverse voices and personas that will resonate best with the target audience.
- AI Code Generators: An AI code generator leverages Large Language Models (LLMs) and deep learning algorithms to process information provided in a text prompt and generate code based on the described functionality. While the concept of automating code writing has been explored for years, recent advancements in Natural Language Processing (NLP) and Generative AI have made it possible to create high-quality, functional code efficiently. This technology simplifies complex programming tasks to make coding more accessible and streamline software development workflows.
