Accuracy

How often an AI model makes correct predictions or decisions.

Algorithm

A step-by-step set of rules or instructions a computer follows to solve a problem.

Artificial Intelligence (AI)

Using computers to perform tasks that normally require human intelligence, like problem-solving and decision-making.

Automation

Using AI or other technology to perform tasks without human intervention.

Bias

When an AI system favors certain outcomes unfairly, often due to biased training data.

Chatbot

An AI system that simulates conversation with humans, often used in customer service or FAQs.

Classification

Sorting data into categories, like whether an email is spam or not.

Data Science

Using data to find patterns, solve problems, and make decisions or predictions.

Deep Learning

A type of machine learning that uses layers of algorithms (like neural networks) to recognize patterns and make decisions.

Ethics in AI

The study of fairness, accountability, and responsibility when designing and using AI.

Explainability

How well an AI system can explain why it made a decision, making it easier for people to understand.

Generative AI

AI that creates new content, like text, images, or music, based on patterns it learned from existing data.

Large Language Models (LLMs)

Advanced AI models trained on huge amounts of text to generate human-like responses in language tasks.

Machine Learning (ML)

A type of AI that uses data to teach computers to make decisions or predictions without being explicitly programmed.

Model

The part of AI that makes decisions or predictions based on what it learned from data.

Natural Language Processing (NLP)

A branch of AI that helps computers understand and work with human language, like chatbots or translation tools.

Neural Networks

Computer systems inspired by the human brain that learn from data to make predictions or classifications.

Overfitting

When an AI model learns patterns too closely from training data and doesn’t perform well on new data.

Precision

The proportion of relevant results the AI model returns out of all the results it returns.

Predictive Analytics

Using data and AI to make predictions about future outcomes.

Prompt Engineering

Creating precise instructions or questions for AI models to get the best responses.

Recall

The proportion of actual relevant results that the AI model successfully found.

Regression

Predicting a numeric outcome based on data, like forecasting sales.

Reinforcement Learning

AI that learns by trial and error, using rewards and penalties to improve over time.

Sentiment Analysis

Using AI to detect the emotional tone behind words, like whether a review is positive or negative.

Supervised Learning

Machine learning where the model learns from labeled examples (data with known answers).

Tokens

In AI language models, these are the small units of text (like words or word pieces) that the AI uses to understand and generate language. For example, "AI Sally" is 2 tokens: "AI" and "Sally".

Training Data

The examples (data) used to teach an AI model to make predictions or decisions.

Transparency

How clearly an AI system shows how it works or how it makes decisions.

Underfitting

When an AI model doesn’t learn enough patterns from the training data and performs poorly.

Unsupervised Learning

Machine learning where the model looks for patterns in data without known answers.