As Artificial Intelligence (AI) becomes increasingly common, it is important to know the basic ins and outs of AI as an evolving technology that is here to stay. This guide is designed to enhance AI literacy within the OU community by offering resources to explore different types of AI, navigate foundational tools and research applications, understand data safety and privacy, and examine the ethical implications of AI use.

Disclaimer: This guide is intended as a living document. We acknowledge that AI technologies are constantly evolving and that the information presented here may become outdated as new tools, best practices, and ethical considerations emerge. This guide will be reviewed and updated regularly, but we encourage readers to critically evaluate the resources and information presented here. 

• Artificial Intelligence: Artificial intelligence (AI) is a system or machine that simulates human intelligence through programming to mimic the behaviors and thought processes of human beings. Modern AI is focused on creating machines that are capable of learning, making decisions, and adapting to tasks through pattern recognition and perception of natural language.
• Machine Learning: Machine learning (ML) is a traditional subset of AI that focuses on tasks related to classification of prediction. ML involves machines to learn from structured datasets and improve/optimize performance over time by recognizing patterns to make decisions and predictions.
  • A Neural Network (NN) is a subset of ML that is made by connecting several layers (that transform data) and "nodes" (that transfer information) to improve speed and accuracy over time.
  • Deep learning is subset of ML that uses multi-layered NNs to transform complex data and automate tasks with minimal human interaction.
• Natural Language Processing: Natural language processing (NLP) is a subset of AI that is used to understand and interpret interactions between machines and human language. NLP includes speech and text processing, natural language generation, and text classification.
• Generative AI: Generative AI (GenAI) broadly encompasses AI models that are designed to respond to user requests, or "prompts," to generate new content such as text, images, and videos. GenAI models are built off deep learning architectures, and predict responses based on the training data.
• Large Language Model: A large language model (LLM) is a type of GenAI that uses machine learning to train on massive amounts of code and text data to interpret human language and generate new text. LLMs are used in generative chatbots and generative pretrained transformers (GPTs).
• Prompt Engineering: A method for giving large language models specific instructions or examples to get the best possible output. How you ask something in a GenAI chat is just as important as what you're asking, since LLMs are trained on datasets that are reflective of the society and language behind them. The practice of writing good prompts with detailed instructions, context, and conversational feedback helps reduce the chance for models to make things up, or "hallucinate."