Generative artificial intelligence (GenAI) can create certain types of images, text, videos, and other media in response to prompts. Here’s what you should know about this growing field and tool.
What is generative AI?
Generative AI, also known as GenAI, enables users to input diverse prompts for the creation of new content, including text, images, videos, sounds, code, 3D designs, and various other media forms. This technology learns and is trained using existing online documents and artifacts. Its evolution is driven by continuous training on expanding datasets, relying on AI models and algorithms applied to extensive unlabeled data sets. The process involves intricate mathematical computations and substantial computing power.
Generative AI adapts and refines its capabilities with each iteration of training on additional data. It relies on complex mathematical operations and significant computational resources to develop an understanding that enables it to predict outcomes akin to human behavior or creative processes.
The surge in the popularity of generative AI can be attributed to its newfound ability to respond to natural language prompts, expanding its applications across various industries. AI generators are now utilized as companions in tasks such as writing, research, coding, designing, and more.
How does generative AI work?
Generative AI models utilize neural networks to recognize patterns in existing data for the generation of novel content. Through training on unsupervised and semi-supervised learning methods, organizations can establish foundational models using large, unlabeled datasets, serving as a groundwork for AI systems to execute tasks [1].
Examples of these foundational models include LLMs, GANs, VAEs, and Multimodal, which power tools like ChatGPT, DALL-E, and others. In the case of ChatGPT, it leverages data from GPT-3 to allow users to create a story based on a given prompt. Another foundational model, Stable Diffusion, empowers users to produce realistic images based on textual input [2].
Generative AI use cases:
- Writing or improving content by producing a draft text in a specific style or length
- Adding subtitles or dubbing educational content, films, and other content in different languages
- Outlining briefs, resumes, term papers, and more
- Receiving a generic code to edit or improve upon
- Summarizing articles, emails, and reports
- Improving demonstration or explanation videos
- Creating music in a specific tone or style
Generative AI offers numerous applications that can enhance our work processes, expediting content creation and minimizing the effort required to formulate an initial survey or email outline. However, it is crucial to address and regulate the limitations of generative AI to avoid potential concerns.
Article sources:
1. Encord.com. "The Full Guide to Foundation Models, https://encord.com/blog/foundation-models."
2. Nvidia.com, "What is Generative AI?, https://www.nvidia.com/en-us/glossary/data-science/generative-ai/."
3. Coursera.org, "What is Generative AI, Definition, Application and Impact", https://www.coursera.org/articles/what-is-generative-ai?/."