Algorithms: Step-by-step instructions or rules designed to solve a problem or perform a task.
Artificial Intelligence (AI): The simulation of human intelligence in machines that are programmed to think and learn.
Bias: In AI, this refers to systematic errors that can occur in a model due to prejudiced assumptions made during the data collection or training process.
Chatbot: A computer program designed to simulate conversation with human users, especially over the internet.
Data Mining: The process of discovering patterns and knowledge from large amounts of data.
Deep Learning: A type of machine learning that uses neural networks with many layers to analyze various factors of data.
Ethics in AI: The study of moral issues and decisions surrounding the development and use of AI technologies.
Generative AI: A type of AI that can create new content, such as text, images, or music, based on the data it has been trained on.
Hallucination: In AI, this refers to when a model generates information or responses that are not based on the input data or reality.
Infodemic: An excessive amount of information about a problem, which makes it difficult to identify a solution. Often used in the context of misinformation.
Large Language Model (LLM): A type of AI model that is trained on a vast amount of text data to understand and generate human-like text.
Machine Learning (ML): A subset of AI where computers use data to learn and make decisions without being explicitly programmed for each task.
Mimicry: The ability of AI to imitate human behavior or patterns.
Natural Language Processing (NLP): A field of AI that focuses on the interaction between computers and humans through natural language.
Neural Networks: Computing systems inspired by the human brain’s network of neurons, used in machine learning to recognize patterns and make decisions.
Overfitting: When a machine learning model performs well on training data but poorly on new, unseen data because it has learned the noise in the training data rather than the actual patterns.
Parameters: Variables in a model that are adjusted during training to improve the model’s performance.
Pattern Detection: The process of recognizing patterns and regularities in data.
Prompt: A piece of text or input given to an AI model to generate a response.
Prompt Engineering: The practice of designing and refining prompts to get the best possible responses from AI models.
Reinforcement Training: A type of machine learning where the model learns by receiving rewards or penalties based on its actions.
Semi-supervised Training: A combination of supervised and unsupervised training, using both labeled and unlabeled data.
Supervised Training: A type of machine learning where the model is trained on labeled data, meaning the correct answers are provided during training.
Temperature: A parameter in AI that controls the randomness of the output. Lower values make the output more focused, while higher values make it more diverse.
Tokens: Pieces of text, such as words or characters, that are used as input for language models.
Training Data: The data used to teach an AI model how to perform a task.
Turing Test: A test proposed by Alan Turing to determine whether a machine can exhibit intelligent behavior indistinguishable from that of a human.
Unsupervised Training: A type of machine learning where the model learns patterns from data without labeled responses.
Validation Data: A subset of the training data used to tune the model’s parameters and prevent overfitting.
This glossary was developed from prompts submitted to Microsoft Copilot and reviewed by our internal Madison College Libraries AI team.
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