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 for using artificial intelligence ethically and with integrity.

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: November, 2024

Before diving into AI tools, take a moment to reflect on why you’re using them.

Ask yourself: is this tool supporting my learning, or am I relying on it to do the work for me?

Remember, each course assignment is designed to help you build skills, think critically, and grow as a student. AI can be a helpful resource, but it’s important to know why you’re using it.

Consider whether you’re using AI to enhance your understanding or just as a shortcut, and think about how creating something of your own first might deepen your knowledge and creativity.

Use of Artificial Intelligence (AI)
At Gateway, we acknowledge the growing use of Artificial Intelligence (AI) in various fields and for
general use. While we encourage faculty and students to explore AI, we require that they adhere
to this policy on the ethical use of this emerging technology

1. Students must cite AI or include a note explaining how much and in what ways their workwas AI-generated. For a formal citation, use the following format: AI Tool Name. (YYYY,Month DD of query). "Text of your query." Generated using AI. www.AI.tool.url.com
2. Students must check any AI content for accuracy and bias. Students are responsible for any inaccurate or biased information.
3. Students are responsible for producing quality work. Poor prompts and inadequate adaptation of AI-produced texts result in poor-quality work.
4. Students must not use AI to cheat or plagiarize by turning in an AI-generated text as original Students submitting an assignment using AI without proper disclosure constitutes academic misconduct.
5. Students must adhere to the instructor’s policies on using AI-generated content in their work. Instructors may prohibit or limit the use of AI on specific assignments or projects.
6. It is the student's responsibility to save and keep the complete transcripts and conversations (recordkeeping) generated using AI. The instructor may request the information to evaluate an assignment.

For further information go to the Academic Integrity and Citation section. 

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.

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.