AI Fundamentals Part 3 : Generative AI (GenAI)

What is Generative AI?

Generative AI, also referred to as "GenAI" is a technology that lets users enter different kinds of prompts to create new media, including text, photos, movies, sounds, code, and 3D designs.
As generative AI is trained on additional data, it develops. It uses artificial intelligence (AI) models and algorithms that are trained on big, unlabeled data sets, which demand sophisticated mathematics and a lot of processing power to produce. These sets of data teach the AI to forecast results in ways that resemble actions or creations made by humans.

How does generative AI work?

Neural networks are used by generative AI models to find patterns in current data in order to produce new material. Organizations can build foundation models from sizable, unlabeled data sets by training them on unsupervised and semi-supervised learning methodologies. This effectively creates a foundation for AI systems to execute tasks.
Foundation models, which power tools like ChatGPT, DALL-E, and more, include LLMs, GANs, VAEs, and Multimodal. ChatGPT allows users to create stories in response to prompts by utilizing data from GPT-3. Stable Diffusion is another foundation model that lets people create realistic visuals from text input.

How to Evaluate Generative AI Models?

Three essential elements are needed for a generative AI model to be successful:
- Quality: Having high-quality generation outputs is essential, particularly for apps that engage directly with consumers. For instance, low-quality speech is hard to understand when it comes to speech creation. Similar to this, while creating photographs, the intended results should be identical to natural photos in terms of appearance.
- Diversity: Without compromising the quality of its generation, a good generative model captures the minority modes in its data distribution. As a result, the trained models' undesirable biases are lessened.
- Speed: In order to be used in content development workflows, many interactive apps, including real-time image editing, require quick generating.

Generative AI use cases

These use cases could inspire you to think of innovative ways that generative AI can help you and your company after you've determined which AI generator best meets your requirements.
- Writing or refining material by creating a draft copy with a particular length or style.
- Including subtitles or translating movies, TV shows, and other stuff into several languages.
- Writing summaries for term papers, resumes, briefings, and more.
- Getting a generic code to work with and modify.
- Condensing reports, emails, and articles.
- Enhancing videos for explanations or demonstrations.
- Composing in a certain style or tone.