Public AI Summit for Educators
Join us on September 16-17 for keynotes and interactive workshops designed to help educators navigate AI with confidence.
AI Guide
Key Terms
| Term | Definition |
| Algorithm | A sequence of instructions for solving a problem or performing a task. Algorithms define how an artificial intelligence system processes input data to recognize patterns, make decisions, and generate outputs. |
| Artificial Intelligence (AI) | Computer systems designed to perform tasks associated with human intelligence, such as pattern recognition or decision making. |
| Chatbot | A program that communicates with humans through text in a written interface, built on top of a large language model. Examples include ChatGPT by OpenAI, Gemini by Google, and more. While many people refer to chatbots and LLMs interchangeably, technically the chatbot is the user interface built on top of an LLM. |
| Foundation Model | A type of model designed to be a general-purpose “foundation” for a wide range of applications. Foundation models can be adapted (or “fine-tuned”) for domain- or task-specific purposes. In contrast, “narrow” models are designed such that they are limited to specific tasks. |
| Generative Artificial Intelligence (GAI) | A subfield of Artificial Intelligence, referring to models capable of generating content (such as language, images, videos, 3D assets, or music). The output of GAI models is based on patterns learned from extensive training datasets. |
| Hallucination | In the context of AI, a falsehood presented as truth by a large language model. For example, the model may confidently fabricate details about an event, provide incorrect dates, create false citations, or dispense incorrect medical advice. |
| Large Language Model (LLM) | A type of generative AI model that works specifically with written language (both natural language and code). The models are trained on massive corpuses of text that have been taken from the Internet. Examples include GPT-5.4 by OpenAI which powers ChatGPT, Claude by Anthropic, Gemini by Google, LLaMA 4 by Meta, and more. |
| Machine Learning | A field of computer science in which a system learns patterns or trends from underlying data. Machine learning algorithms perform tasks like prediction or decision making. |
| Neural Network | A type of computational model (named due to the design elements that loosely resemble elements of the human brain, including interconnected nodes and layers), which can be trained to recognize patterns and make predictions. During training, the connections between different layers of the neural network are iteratively strengthened or weakened based on input “training data” and other model design parameters. |
| Prompt | In the context of AI, it is the input text, video clip, PDF, or voice command supplied by a human to a generative AI model. The prompt often describes what you are looking for, but may also give specific instructions about style, tone, or format. |
| Reinforcement Learning from Human Feedback (RLHF) | A technique that trains a model directly from human feedback. RLHF is often used in tasks where it’s difficult to define a clear, algorithmic solution but where humans can easily judge the quality of the model’s output. With generative AI models, RLHF is one method used to identify and filter out problematic content like violence and hate speech. |
| Training Data | The content used to teach a machine learning system how to perform a particular task. Training data gives the system a knowledge base from which the model can make predictions or identify patterns. Training data might include images, text, code, or other types of media. It can be structured or unstructured, depending on the type of training process being used. |