Summary
This document provides a concise glossary of common AI vocabulary and terms, designed to help readers quickly understand essential concepts in artificial intelligence. Whether you're new to AI or looking to brush up on industry language, this guide offers clear, straightforward definitions to build familiarity with key ideas and technologies.
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Overview | Common AI Terms and Definitions | Resources
Overview
This document provides a concise glossary of common AI vocabulary and terms, designed to help readers quickly understand essential concepts in artificial intelligence (AI). Whether you're new to AI or looking to brush up on industry language, this guide offers clear, straightforward definitions to build familiarity with key ideas and technologies.
Common AI Terms and Definitions
AI Bias
- Also called machine learning bias or algorithm bias, refers to the occurrence of biased results due to human biases that skew the original training data or AI algorithms. This leads to distorted outputs and potentially harmful outcomes.
Algorithm
- “A process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer” - Oxford Dictionary
Artificial Intelligence (AI)
- "AI is a branch of computer science aimed at creating machines that mimic human intelligence. It's used to perform tasks that usually require human thought, like understanding language, recognizing patterns, or making decisions." - ISTE Mini ebook on AI, Bringing AI to Schools: Tips for School LeadersNeural Network
Chatbot
- A chatbot is a computer program or AI application designed to simulate human conversation, providing automated responses to user queries or interactions, typically through text or speech.
Constitutional AI
- A framework for designing AI systems that adhere to ethical guidelines and societal principles, aiming to ensure safe, fair, and responsible use.
Deep Learning
- A technique that structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own. Deep learning is especially effective in recognizing patterns and making classifications or predictions.
Digital Citizenship
- The ability to recognize the rights, responsibilities and opportunities of living, learning and working in an interconnected digital world; acting and modeling in ways that are safe, legal and ethical in online digital spaces.
Generative AI (GenAI)
- Creates new content and generates ideas from learned data patterns, such as with ChatGPT, text to image in Canva, or video creation features in AdobeExpress.
Hallucinations in AI
- This term refers to errors in AI output, typically in generative models, where the AI produces data that is significantly different from what it was trained on. It occurs due to various factors including overfitting, biases in the training data, or flaws in the model architecture.
Integrations (AI)
- The process of incorporating AI capabilities into various systems or applications to enhance functionality and automate complex tasks.
Large Language Model
- A large language model (LLM) is a type of artificial intelligence (AI) model that has been trained to recognize and generate vast quantities of written human language
Learn Platform
- The Learn Platform is a website designed to support educators and other stakeholders in knowing which apps, websites, and extensions have been reviewed for use in Canyons School District. Technology tools will be approved through Learn Platform if they follow FERPA, CIPA, and COPPA.
Neural Network
- Neural networks, also known as artificial neural networks (ANNs), are at the heart of AI as the algorithms that power it. Neural networks are named and structured after the pattern of a human brain, mimicking the way biological neurons signal to one other. It is this signaling method that is used to process data in a way similar to humans. Neural networks rely on training and data to learn and improve over time. - IBM, What Are Neural Networks?
Overfitting
- When a model performs well on training data but poorly on unseen data.
Predictive AI
- Analyzes a data set to learn and predict future events or decisions, such as Netflix suggestions or automatically suggesting or finishing a sentence.
Prompt
- A prompt is a natural language text that requests generative AI to perform a specific task.
Prompt Engineering
- Prompt engineering is the process where you guide generative artificial intelligence (generative AI) solutions to generate desired outputs. Even though generative AI attempts to mimic humans, it requires detailed instructions to create high-quality and relevant output. In prompt engineering, you choose the most appropriate formats, phrases, words, and symbols that guide the AI to interact with your users more meaningfully. Prompt engineers use creativity plus trial and error to create a collection of input texts, so an application's generative AI works as expected.
Reactive AI
- Responds to data (or inputs) without learning from that data, such as adaptive testing, Chess.com, or Alexa.
Safety Vocabulary
- Personally Identifiable Information (PII)
- A term used in education records to describe information that can be used to identify a student, such as their name, date of birth, or identification number.
- Data Privacy
- The set of practices and concerns centered around the ethical collection, storage, and usage of personal information by artificial intelligence (AI)- systems.
- Data Privacy Agreement (DPA)
- A legal contract between a school and a company that outlines how student data should be handled. DPAs are designed to protect student privacy and ensure that vendors comply with data protection regulations.
- Walled Garden
- A closed digital ecosystem where content and applications are restricted and controlled by a single provider, limiting external access and interoperability.
Additional Resources