Artificial Intelligence (AI)

This guide provides an introduction to artificial intelligence (AI), with a focus on generative AI. You’ll find explanations of the benefits and limitations as well as support to use, cite, research, and teach with artificial intelligence.

AI tools and the legal and ethical landscape surrounding their use are changing rapidly. We will periodically update this guide and provide the date of last update to inform your use.

Updated: February 2024

These definitions were generated using the Llama 2 large language model and reviewed for accuracy by a Libraries staff member. Generating content like this can be done efficiently using a large language model, but it is important to remember to review the output carefully and acknowledge the source.

Artificial Intelligence (AI)

Artificial intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and natural language understanding.

Types of Artificial Intelligence

• Chatbot

A chatbot is a software application that uses natural language processing (NLP) and machine learning to simulate conversation with humans, either via text or voice interfaces.

• Generative AI

Generative artificial intelligence refers to algorithms and models that can generate new content or data, such as images, videos, music, or text, based on patterns learned from existing information.

• Machine Learning (ML)

Machine learning is a subset of artificial intelligence that involves training computer systems to learn from data and improve their performance over time through experience.

• Natural Language Processing (NLP)

NLP is a subfield of artificial intelligence that deals with the interaction between computers and human language, including text and speech processing, sentiment analysis, machine translation, and dialogue systems.

• Large Language Model (LLM)

A large language model is a type of machine learning model that is trained on vast amounts of text data to generate language outputs that are coherent and contextually appropriate.

Using Large Language Models (LLMs)

• Hallucination

In the context of AI, hallucination refers to the phenomenon where a model generates inaccurate or imaginary output that cannot be explained by its training data, often due to overfitting or underfitting.

• Prompt

A prompt is a specific task or question that is given to an AI system to elicit a response or output.

• Prompt Engineering

Prompt engineering is the process of designing and refining prompts to elicit desired responses or behaviors from AI systems, in order to improve their performance and versatility.

Understanding Large Language Models (LLMs)

• Parameters

Parameters are settings or values that are adjusted during the training process to optimize the performance of an AI model, such as the learning rate, regularization strength, or number of hidden layers.

•  Temperature

In the context of generative AI, temperature refers to a parameter that controls the "randomness" or "diversity" of generated samples, with higher temperatures resulting in more diverse and less predictable outputs.

• Tokens

In Natural Language Processing and machine learning, tokens refer to individual words or phrases in a text dataset, which are used as input features for models to analyze and understand the meaning of the text.

• Training Data

Training data is the set of examples or inputs used to train an AI system, which helps the model learn patterns and relationships in the data and make predictions or decisions.