A Glossary of AI Terms Everyone Should Know

Artificial Intelligence (AI)

AI refers to machines or software that perform tasks requiring human-like intelligence — such as reasoning, learning, and problem-solving. It powers chatbots, self-driving cars, and creative tools that write or draw.

Algorithm

An algorithm is a set of rules that computers follow to solve problems. Search engines, recommendation systems, and AI assistants all rely on algorithms to make decisions based on your data.

Bias

AI bias happens when models produce unfair results due to incomplete or unbalanced training data. For example, an AI that’s only trained on Western voices may struggle to understand African or Asian accents.

Conversational AI

These are AI systems that interact through conversation — like ChatGPT, Siri, or Alexa. They use Natural Language Processing to understand and respond to human speech.

Data Mining

The process of scanning huge data sets to uncover trends, patterns, or correlations. Businesses use data mining to predict customer behavior and improve performance.

Deep Learning

A type of Machine Learning that uses multi-layered neural networks to analyze data. Deep learning is what allows AI to recognize images, detect objects, and even drive cars.

Large Language Model (LLM)

An LLM is an AI system trained on vast amounts of text to understand and generate language. Models like GPT-4 and Gemini are prime examples that power chatbots and text-based AI tools.

Generative AI

This type of AI creates things — images, text, music, or even videos — based on prompts. Tools like DALL·E and ChatGPT are popular examples.

Hallucination

An AI hallucination occurs when the system generates false or misleading information that sounds believable. Always double-check facts from AI-generated outputs.

Image Recognition

AI’s ability to identify objects or features within images. Used in medical imaging, facial recognition, and even wildlife tracking.

Machine Learning (ML)

The process where machines learn from data and improve over time. It’s the foundation of most AI systems today.

Natural Language Processing (NLP)

The branch of AI that enables computers to understand, interpret, and generate human language. It’s how your phone can respond when you say, “Remind me to call Dad at 7 p.m.”

Neural Networks

Inspired by the human brain, neural networks are the backbone of modern AI. They consist of interconnected layers that help machines process complex information and learn patterns.

Optical Character Recognition (OCR)

OCR allows computers to extract and read text from images or scanned documents. For example, converting a photo of a printed page into editable text.

Prompt Engineering

The skill of writing precise and creative instructions (prompts) to get accurate or innovative responses from AI systems like ChatGPT or Claude.

Reinforcement Learning from Human Feedback (RLHF)

This is how AI models learn from human correction. When an AI gets an answer wrong, humans guide it to the right one — refining its accuracy and behavior.

Speech Recognition

The technology that converts spoken language into text or commands. Common in transcription software and virtual assistants.

Token

AI breaks text into small pieces called tokens — these are the “words” AI understands. Costs for tools like the OpenAI API are often based on the number of tokens processed.

Training Data

The information AI systems learn from. Better and more diverse data make models more accurate and fair.

Turing Test

Created by Alan Turing, the Turing Test checks whether a machine can think like a human. If a person can’t tell they’re talking to a machine, the AI passes.