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Navigating Technology [TEST] : Generative AI
Generative Artificial Intelligence - Module 3 of 4
Generative AI (GenAI) is a type of Artificial Intelligence that generates content based on prompts written in natural, conversational language, producing everything from text responses to images. Unlike traditional search engines that simply retrieve existing content, GenAI generates text and visuals by analyzing patterns in data gathered from sources like websites, social media, and other digital media. By learning typical word and image associations, it can generate responses and visuals that feel contextually relevant and tailored to user input.
This module explains how GenAI works, explores its wide range of applications, and highlights its growing impact across various fields. It also provides important cautions for responsible use, including tips on maintaining academic integrity when using GenAI tools in your work.
How Does it Work?
- Training Data GenAI is trained on large datasets that include text, images, or other types of information, using large language models (LLMs) to process and generate responses. These datasets are sourced from a wide range of materials, books, websites, and social media.
- Learning Patterns During training, the AI uses algorithms to analyze this data and learn patterns and relationships between different elements, such as how words are commonly used together or how certain visual features appear in images.
- Generating Content When given a prompt or input, GenAI uses what it has learned to generate content. Models like GPTs (Generative Pre-trained Transformers) are the advanced algorithms behind this ability; they are trained on large datasets to understand and predict language patterns. are trained on large datasets to understand and predict language patterns. For example, if you ask it to write a story, the model predicts what words or phrases are likely to follow based on patterns it has seen before.
- Refinement The generated content can be refined and improved through various techniques, such as adjusting the model or incorporating feedback, to better align with user expectations and accuracy.