AI Vocabulary

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Understanding AI vocabulary is crucial in today's rapidly evolving world. As AI technology continue to integrate into classrooms and learning environments, understanding key AI terms enables educators to effectively incorporate AI tools into their curricula and guide students in their responsible use. Familiarity with AI concepts allows both teachers and students to engage in meaningful discussions about the ethical implications of AI, fostering a generation of informed digital citizens.

If you would like to learn how the pieces of AI vocabulary fit together, check out this quick AI overview or a more comprehensive exploration of AI for educators.

Essential AI Vocabulary

  • Artificial Intelligence (AI): Like a super-smart computer program that can learn, reason, and perform tasks that typically require human intelligence. It's designed to process vast amounts of information, recognize patterns, and make decisions or predictions based on that data.

  • Diffusion Models: A type of generative AI model that creates new data (like images) by gradually removing "noise." Includes tools like DALL-E, Stable Diffusion, and Midjourney.
  • Foundational Models: Large AI systems trained on vast amounts of data that can be adapted for various tasks.
  • Generative AI: Artificial intelligence systems that use deep learning to create new content, such as text, images, or audio.
  • GPT (Generative Pre-Trained Transformers): A type of AI model that's really good at understanding and generating human-like text. Think of it as a super-advanced autocomplete.
  • Hallucinations: When AI generates plausible sounding but incorrect information.
  • Large Language Models (LLMs): A type of foundational model specifically designed to understand and generate human-like text, answer questions, and perform various language-related tasks.
  • Machine Learning: A branch of computer science that uses statistical techniques to give computer systems the ability to "learn" from data without being explicitly programmed.
    • Supervised Learning: A type of machine learning where the AI model is trained on labeled data. This is the type of AI homework where we give them all the answers. Data are labeled so that the model learns to understand the association between inputs and outputs. For example, it might learn to identify cats in photos by studying many images labeled "cat" or "not cat." 
    • Unsupervised Learning: A machine learning technique where the AI model is given unlabeled data and must find patterns or structure on its own. 
    • Reinforcement Learning: A training method where an AI agent learns to make decisions by interacting with an environment. The agent receives rewards or penalties for its actions, allowing it to learn optimal behavior through trial and error. 
    • Deep Learning: A subset of machine learning that uses artificial neural networks with multiple layers (hence "deep"). These networks can learn hierarchical representations of data, enabling them to tackle complex tasks like image recognition, natural language processing, and more. 
  • Natural Language Processing (NLP): A branch of artificial intelligence that focuses on the interaction between computers and human language. It involves the ability of a computer program to understand, interpret, manipulate, and generate human language in a valuable way. NLP combines computational linguistics—rule-based modeling of human language—with statistical data, machine learning, and deep learning models.
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    Neural Networks: Computing systems that are modeled after the human brain. They consist of interconnected "neurons" that process and transmit information, called "nodes."
  • Parameters: The internal variables of a machine learning that the model uses to make predictions or decisions. They are essentially the "knowledge" or "skills" that the AI model learns during its training process. A key concept in discussions about AI capabilities and limitations.
  • Prompt Engineering: The art of crafting effective instructions or questions to get the best results from AI.
  • Tokens: The building blocks that the AI model uses to understand and generate text. They provide a digital stand-in in the form of 0s and 1s.

Learn More

Ready to learn more? Terrific! Check out this quick AI overview or a more comprehensive exploration of AI for educators to understand how the pieces of AI vocabulary fit together. When you're ready, consider the basics of prompt engineering or explore the K-12 prompt library.