Audience: teachers, student-learners, community mentors, and beginner builders using Incubator.org.
Use this guide: Every term has a short, human-readable definition plus at least one way to try it yourself using widely-used tools (most are free or have generous tiers). Scan the glossary for fast definitions, then jump to the Try it / Tools snippets to actually do something with each idea. Copy the prompt boxes into your AI tool of choice and adapt them for class, self-study, or a workshop.

Quick Glossary (plain English)

1. Bias — When an AI prefers or treats some things unfairly because of patterns in its training data.
2. Label — The “answer” attached to data (e.g., a cat/not-cat tag).
3. Model — The trained program that makes predictions or generates content.
4. Training — Teaching a model using examples so it improves.
5. Chatbot — A program that converses by text or voice.
6. Dataset — A large, structured collection of examples used for training/evaluation.
7. Algorithm — A step-by-step method to solve a problem.
8. Token — A chunk of text (or data unit) used by language models.
9. Overfitting — When a model memorizes the training set and fails on new data.
10. AI Agent — Software that can plan and act (often across tools/APIs) toward a goal.
11. AI Ethics — Principles and practices to make AI fair, safe, and accountable.
12. Explainability — Ways to understand why a model made a decision.
13. Inference — Running the trained model to get outputs (predictions/generations).
14. Turing Test — A thought experiment: can a machine’s responses pass for human?
15. Prompt — The instruction or input you give an AI system.
16. Fine-Tuning — Further training a model on your specific data or style.
17. Generative AI — Models that create text, images, audio, or video.
18. AI Automation — Using AI to complete multi-step tasks without constant supervision.
19. Neural Network — A model architecture loosely inspired by brain neurons.
20. Computer Vision — AI that interprets images or video.
21. Transfer Learning — Starting from a pretrained model and adapting it to a new task.
22. Guardrails — Controls to keep outputs safe, on-topic, and policy-compliant.
23. Open-Source AI — Models/tools whose code/weights are openly shared.
24. Deep Learning — Neural networks with many layers that learn complex patterns.
25. Reinforcement Learning — Training by trial, error, and rewards.
26. Hallucination — When an AI confidently makes up facts.
27. Zero-Shot Learning — Doing a new task without training examples, guided by the prompt.
28. Speech Recognition — Turning spoken language into text.
29. Supervised Learning — Training with labeled examples.
30. Model Context Protocol (MCP) — A standard for letting models securely use local tools/data.
31. Machine Learning — Letting computers learn patterns from data.
32. AI (Artificial Intelligence) — Systems that perform tasks we associate with human intelligence.
33. Unsupervised Learning — Finding patterns in unlabeled data.
34. LLM (Large Language Model) — Big text models that read/write code, essays, etc.
35. ASI (Artificial Superintelligence) — Hypothetical AI vastly beyond human general ability.
36. GPU (Graphics Processing Unit) — Hardware that speeds up AI training/inference.
37. NLP (Natural Language Processing) — AI that works with human language.
38. AGI (Artificial General Intelligence) — Hypothetical AI that can learn anything a human can.
39. GPT (Generative Pretrained Transformer) — A popular LLM family trained then adapted for tasks.
40. API (Application Programming Interface) — A standard way apps/services talk to each other.