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Generative AI Faculty Research Guide

--Intro to generative AI and explaining its impact on academic integrity and scholarship

What is Generative AI?

  • Generative AI, short for Generative Artificial Intelligence, refers to a class of artificial intelligence algorithms and models that can generate new content, such as images, text, music, videos, and more, that is similar to the data it was trained on. Unlike traditional AI systems that are designed to recognize patterns or make decisions based on existing data, generative AI is capable of creating new data from scratch.
  • One of the fundamental techniques used in generative AI is the generative model, which is a type of machine learning model that learns to model the underlying distribution of the training data. These models are trained on large datasets and learn the patterns and features within that data in order to generate new data points that resemble the original data.
  • Generative AI has found applications in various fields, such as creative content generation, data augmentation for training other AI models, generating realistic images for computer graphics, and even assisting in drug discovery through molecule generation.
  • However, it's essential to note that with the power to generate realistic content, generative AI also raises concerns about potential misuse, such as deepfake generation and fake news propagation. As the technology advances, ethical considerations and responsible use become critical to harness its benefits responsibly.
Have ideas or suggestions? We encourage recommendations for additional resources not currently listed in this guide. Members of the college community are encouraged to email suggestions to library@westmoreland.edu

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