Artificial Intelligence Literacy: Introduction

Artificial Intelligence - the use of computers to model the behavioral aspects of human reasoning and learning ("artificial intelligence"). AI uses algorithms, such as logic, pattern recognition, and machine learning to perform like human intelligence. AI can solve complex problems and make art. AI is being integrated into many applications with a variety of uses and fields. 

ChatGPT - A chatbot that can generate human-like text based on conversational prompts from a user. It is based on a large language model, version GPT-4 as of 14 March 2023, and deep learning. GPT stands for Generative Pre-trained Transformers which is a type of neural network that can be trained on large amounts of data. 

Hallucination - When an AI tool generates a confident response but the answer is false. This occurs because the information was not available in the training data. This can also occur if the training data was biased. The algorithm will create an answer with a high level of confidence and then go on to repeat that false answer. This is seen when an AI tool gives sources that do not exist or very skewed, or biased answers. This is why it is important to fact-check AI responses. 

Large language models (LLMs) - It "is a deep learning algorithm that can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets" (Lee). LLMs like GPT-3 use neural networks to train on a large amount of data and form predictions based on probability. They have a wide variety of uses from text and image generation to software development and coding. ChatGPT is an example. 

Machine learning - A type of artificial intelligence that allows computers to be trained from data rather than being explicitly programmed. 

Neural networks - A type of machine learning algorithm that is modeled after the human brain. It consists of interconnected nodes that process information and make predictions. Neural networks are used in artificial intelligence applications to facilitate image recognition, natural language processing, and speech recognition. 

Prompts - A method of communicating with Large Language Models like ChatGPT or BingChat to generate a response. The user formulates a question or statement to initiate a response from the AI tool. 

Training datasets - The data used to train machine learning models. The datasets teach the model to make predictions based on probability. The quantity and quality of training data are critical factors in determining the reliability and performance of a machine learning model. Training models are a fixed set of data. New, often larger datasets are released periodically which prompts developers to train new and improved models. This is why AI tools like ChatGPT release new versions.  

Sources

"Artificial intelligence." The Columbia Encyclopedia, Paul Lagasse, and Columbia University, Columbia University Press, 8th edition, 2018. Credo Reference, Accessed 29 Mar. 2023.

Lee, Angie. “What Are Large Language Models Used for and Why Are They Important?” NVIDIA Blog, 26 Jan. 2023, https://blogs.nvidia.com/blog/2023/01/26/what-are-large-language-models-used-for/.