ChatGPT and AI Tools Faculty Research Guide

In a talk from the cutting edge of technology, OpenAI cofounder Greg Brockman explores the underlying design principles of ChatGPT and demos some mind-blowing, unreleased plug-ins for the chatbot that sent shockwaves across the world. After the talk, head of TED Chris Anderson joins Brockman to dig into the timeline of ChatGPT's development and get Brockman's take on the risks, raised by many in the tech industry and beyond, of releasing such a powerful tool into the world. The Inside Story of ChatGPT's Astonishing Potential by Greg Brockman is licensed CC-BY-NC-ND4.0 International.

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.

For more terms: www.aiprm.com/ai-glossary/

Artificial Intelligence (AI): A field of study within computer science, focused on the development of computer systems that can accomplish tasks typically associated with human intelligence. These tasks include speech recognition, route mapping, decision making, etc.

Bias: The training data of an AI model can skew the output, leading it to generate inaccurate or offensive material.

Chatbot: A program designed to communicate with humans in a natural manner, sometimes to facilitate providing information or completing tasks.

Chat Generative Pre-trained Transformer (ChatGPT): A chatbot developed by OpenAI. ChatGPT is a transformer type of AI that is designed to mimic conversations using natural language processing, through which users can write prompts to generate text-based responses.

Generative AI: A model of artificial intelligence that can generate new content such as text, images, video, etc., through pattern recognition, by examining large amounts of training data and creating material that contains similar characteristics to identified patterns in the dataset. Examples include ChatGPT, Claude, Midjourney or DALL-E.

Hallucinations: Instances where a generative AI model generates output that contains inaccurate or irrelevant information, especially when it may look correct. For example, when asking ChatGPT (or any text-based generative AI) to generate a list of citations for a topic, the citations it provides may look accurate but the source material associated with the citation may not actually exist when searching for it.

Large Language Model (LLM): An AI model that receives large amounts of training data that establishes the capacity for it to respond to conversational queries. AI such as ChatGPT, Bard, or Claude use LLM.

Natural Language Processing (NLP): The programmed capacity to understand conversations and respond in kind.

Prompt: A structured text-based query that asks a generative AI to generate new content in the form of text, image, video, etc.

Prompt Engineering: The process of refining prompts to elicit more desirable results from generative AI.

Training Data: The development of a generative AI model involves the input of specific types of data, often in large amounts. This process is referred to as “training” and it determines the content output of the specific model. For example, if developing an AI that reviews artwork specifically, the AI model will be trained only on data containing artwork.

This glossary is an adaptation of the following sources: Generative AI as a Research Tool from Brown University and Librarians Can Play a Key Role Implementing Artificial Intelligence in Schools from School Library Journal.

This guide was created by Missy Comer, Librarian at Tidewater Community College in Portsmouth, Virginia. Permission to revise and use was granted on October 5, 2023.