AI for Non-IT Library

AI Without the Nerd Stuff: foundational AI concepts explained for non-technical humans, using real-life analogies and plain-English examples.

Published lessons

  • AI Agents: AI systems that can plan, decide, and take actions toward a goal.
  • AI Hallucination: When AI confidently gives an answer that may be wrong or made up.
  • API: A bridge that lets one software tool talk to another.
  • Artificial Intelligence: Technology that lets machines do things that normally need human intelligence.

Coming soon

  • AI Ethics: The rules, risks, and responsibilities behind building and using AI safely.
  • Attention Mechanism: How AI decides which words or details matter most in a sentence.
  • Automation: Using technology to complete repetitive work with less human effort.
  • Chatbot: A tool that can respond to people through conversation.
  • Computer Vision: The branch of AI that helps machines understand images and videos.
  • Deep Learning: A type of machine learning inspired by how layers of the brain process information.
  • Embeddings: AI's way of turning words, images, or ideas into numbers that capture meaning.
  • Fine-Tuning: Training an existing AI model further so it becomes better at a specific task.
  • Generative AI: AI that can create new text, images, audio, video, or code.
  • GPT: A type of language model that predicts and generates human-like text.
  • Image Generation: AI that creates images from text prompts or visual references.
  • Large Language Model: An AI model trained on huge amounts of text to understand and generate language.
  • Machine Learning: A way for computers to learn patterns from data instead of being manually programmed.
  • Model: The trained AI system that makes predictions, generates answers, or performs tasks.
  • Multimodal AI: AI that can understand more than one type of input, like text, images, audio, or video.
  • Natural Language Processing: The field of AI that helps machines understand human language.
  • Neural Network: A layered system that helps AI learn patterns from data.
  • Prompt: The instruction or question you give to an AI tool.
  • Prompt Engineering: The skill of writing better instructions to get better AI outputs.
  • RAG: A method that helps AI answer using external documents or knowledge sources.
  • Reinforcement Learning: A learning method where AI improves through rewards and feedback.
  • Speech Recognition: AI that converts spoken words into text.
  • Tokens: The small pieces of text that AI reads and processes.
  • Training Data: The information used to teach an AI model how to understand patterns.
  • Transformer: The architecture behind many modern AI tools, including large language models.
  • Vector: A list of numbers that represents meaning, features, or position.
  • Vector Database: A database that stores meaning-based representations so AI can find similar things.
  • Workflow Automation: Connecting tools and steps together so work moves automatically.

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