Artificial Intelligence and Generative AI for Media & Journalism: Home

This guide is a work in progress and will continue to be updated as new resources, research, and ideas are published.

This guide provides an introduction to AI and Generative AI, focusing on their applications in academia. It emphasizes the importance of understanding these technologies and their potential impact on research, instruction, and study.

This guide aims to serve as a comprehensive resource for students, faculty, and library staff to navigate the evolving landscape of AI and Gen AI in academic settings, and explain what artificial intelligence and generative AI are and how they work. It seeks to educate and empower users to effectively utilize these technologies while addressing the associated challenges and ethical considerations. (Text generated by Microsoft's Copilot).

UNC-Chapel Hill has provided some guidance on using generative AI in the classroom. You can read our university's guidelines here.

In this guide you will find:

• AI Literacy Resources - information to help you understand what AI literacy is and why it's important.
• AI Tips and Tools - a collection of tips on using AI and some tools to help with class work.
• Libraries and AI - resources showing how libraries can help their patrons better understand and use AI.
• Students and AI - some guidance on how students can and should use AI in the classroom.
• Faculty and AI - information, tools, and insight on how faculty can use AI in the classroom.
• AI and Journalism - how AI and journalism intersect, and what the future of journalism looks like with generative AI.
• How to Cite AI and Plagiarism Concerns - some resources on how to cite AI and some information on plagiarism concerns when using generative AI.

This subject guide is adapted from a LibGuide created by Melissa S. Del Castillo, Virtual Learning & Outreach Librarian at FIU Libraries.

Simply put, it's a form of technology that aims to simulate human intelligence and thinking through machines and computers. Everyday examples of AI include your phone's digital assistant, autonomous vehicles, and GPS. Machine learning and deep learning are sub-sections of AI. Both types of learning use neural networks, modeled on the human brain, to "learn" through taking in large amounts of data. Generative AI, unlike traditional AI, learns from data and can create its own content based on that information. Traditional AI works off of pre-set rules. Generative AI uses deep learning to take in large amounts of raw data, like all of Shakespeare's works, and then produce statistically probable outputs when prompted. Open AI's Chat GPT, Microsoft's Copilot, Google's Gemini, and Dall-E are popular examples of generative AI programs.

Some AI-related Terms to Know

Deep learning: Like neural networks, deep learning is modeled on the way the human brain works and powers many machine learning uses, like autonomous vehicles, chatbots, and medical diagnostics. Deep learning is similar to any kind of machine learning, where an algorithm is fed examples and learns to produce output that resembles the examples it learned from. (Think of a baby or puppy putting everything it comes across in its mouth. As it goes, it learns what it can and can’t eat).

Large language models (LLMs): LLMs use deep learning to take in large amounts of data or text and are then able to train on that data to generate human-like responses, text, or other tasks.

Machine learning: A subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed.

Neural network: Artificial neural networks are modeled on the human brain, in which thousands or millions of processing nodes are interconnected and organized into layers.

Strong AI: As of right now, strong AI is purely theoretical. Strong AI is the idea that a machine could have the same intelligence and consciousness as a human.

Weak AI: Also known as narrow AI, weak AI is AI that is trained on specific tasks.

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