AI Glossary
New to AI? Here are simple definitions for common terms you will encounter.
Artificial Intelligence (AI)
The simulation of human intelligence processes by computer systems, including learning, reasoning, and self-correction.
Machine Learning (ML)
A subset of AI where systems learn and improve from experience without being explicitly programmed, using algorithms to identify patterns in data.
Large Language Model (LLM)
An AI model trained on vast amounts of text data, capable of understanding and generating human-like text. Examples include GPT-4, Claude, and Gemini.
Neural Network
A computing system inspired by the human brain, consisting of interconnected nodes (neurons) that process information in layers.
Prompt
The input text or instruction given to an AI model to generate a response. Effective prompting is key to getting useful AI outputs.
Hallucination
When an AI model generates false or nonsensical information that appears plausible. This is a known limitation of current AI systems.
Token
The basic unit of text that AI models process. A token can be a word, part of a word, or punctuation. Models have token limits for input/output.
Fine-tuning
The process of further training a pre-trained AI model on specific data to specialize it for particular tasks or domains.
API (Application Programming Interface)
A way for different software applications to communicate. AI APIs allow developers to integrate AI capabilities into their applications.
Generative AI
AI systems that can create new content including text, images, audio, and video. Examples include ChatGPT, DALL-E, and Midjourney.
Natural Language Processing (NLP)
A branch of AI that enables computers to understand, interpret, and generate human language.
Computer Vision
An AI field that enables computers to interpret and understand visual information from the world, such as images and videos.
