Glossary of Key GenAI and ML Terms for General Users
| Term | Definition |
|---|---|
| AI (Artificial Intelligence) | Technology that mimics human intelligence to perform tasks like decision-making, language understanding, and pattern recognition. |
| AI Performance Paradox | A phenomenon in which improvements in AI systems’ performance are outpaced by rising human expectations, causing people to perceive the AI as underperforming or disappointing despite objective gains. |
| Algorithm | A set of rules or instructions used by AI systems to analyze data and make decisions. |
| Bias (in AI) | Systematic errors in AI models are caused by flawed data or assumptions, which can lead to unfair outcomes. |
| Computer Vision | AI that enables machines to interpret visual data like images and videos. |
| Dataset | A structured collection of data used to train and test AI models. |
| GenAI (Generative AI) | AI that creates new content (text, images, code) based on patterns it has learned. Examples: ChatGPT, Copilot. |
| Inference | The process of using a trained model to make predictions on new data. |
| LLM (Large Language Model) | A type of GenAI trained on massive text datasets to understand and generate human-like language. |
| Machine Learning (ML) | A subset of AI where systems learn from data to make predictions or decisions without being explicitly programmed. |
| Neural Network | A computing system inspired by the human brain, used in deep learning to process complex data. |
| NLP (Natural Language Processing) | AI that enables machines to understand and generate human language. |