AI glossary for business owners: 26 terms in plain English
By Rodney Noriega, Founder, Universal Enablement · Published Oct 12, 2026 · Updated Oct 12, 2026
Short answer
This glossary explains the AI terms business owners hear most, in a sentence or two each. Use it as a quick reference, and follow the links for the full explanation.
| Term | Plain-English definition | Full piece |
|---|---|---|
| AGI | Artificial general intelligence: AI that could do almost any thinking task a person can. No one has shown it exists yet. | 11 |
| AI (artificial intelligence) | Technology that does tasks that normally need human thinking. | 2 |
| AI assistant | A chat tool that writes and answers questions, such as ChatGPT, Claude, Gemini, Microsoft Copilot or Perplexity. | 1 |
| Attention | How AI reads every word in light of the others to work out what you mean. | 6 |
| Bias (in AI) | Unfair patterns AI picks up from its training data and can make worse. | 13 |
| Deep learning | A type of machine learning that uses many layers of simple calculations. | 2 |
| ELIZA effect | The habit of assuming a chatbot understands us and treating it like a person. | 15 |
| Embedding | A way of turning words into numbers so AI can find things by meaning. | 5 |
| Generative AI | AI that creates new text, images, audio or code. | 2 |
| GPT | Generative pre-trained transformer: OpenAI's family of models behind ChatGPT. | 1 |
| Hallucination | When AI confidently states something false. | 7 |
| Human-in-the-loop | A person checks AI's work before it counts. | 22 |
| Large language model (LLM) | The engine inside AI assistants, trained on huge amounts of text to predict the next word. | 1 |
| Machine learning | AI that learns patterns from examples instead of following written rules. | 2 |
| Mechanistic interpretability | Research trying to understand what happens inside an AI model. | 8 |
| Narrow AI | AI that's good at one specific job. | 2 |
| Neural network | Layers of simple connected calculations, loosely inspired by the brain, that learn from examples. | 8 |
| Parameters | The internal settings an AI model learns. Large models have billions. | 8 |
| Prompt | The instructions or question you give an AI tool. | 6 |
| Reinforcement learning | Learning by reward and correction: what works becomes more likely. | 12 |
| Responsible AI | Using AI in ways that are fair, safe, honest and actually helpful. | 24 |
| Rules-based automation | Software that follows steps a person wrote, with no learning involved. | 10 |
| Temperature | A setting that controls how consistent or creative AI's answers are. | 4 |
| Token | A small chunk of text that AI tools read, write and charge by. | 3 |
| Training data | The text, images or other material an AI model learned from. | 9 |
| Turing test | A test of whether people can tell a machine from a person in a text chat. | 16 |
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