Beginners Guide to AI Terminology

Introduction

Understanding AI terminology is the first step to engaging with this transformative technology. This guide explains key terms in plain language.

Core Concepts

Artificial Intelligence (AI)

Systems that can perform tasks typically requiring human intelligence.

Machine Learning (ML)

AI systems that learn from data rather than explicit programming.

Deep Learning

Machine learning using neural networks with many layers.

Neural Network

Computing systems inspired by the human brain's structure.

Learning Types

Supervised Learning

Learning from labeled examples.

Unsupervised Learning

Finding patterns in unlabeled data.

Reinforcement Learning

Learning through trial, error, and rewards.

Model Concepts

Training

The process of teaching a model using data.

Inference

Using a trained model to make predictions.

Parameters

The values a model learns during training.

Hyperparameters

Settings chosen before training begins.

Common Terms

Algorithm

A set of rules or instructions for solving a problem.

Dataset

A collection of data used for training or testing.

Features

Input variables used to make predictions.

Label

The output or answer the model is trying to predict.

Advanced Concepts

Transformer

Architecture behind modern language models.

Attention Mechanism

Technique for focusing on relevant parts of input.

Fine-tuning

Adapting a pre-trained model for specific tasks.

Prompt

Input text given to a language model.

Performance Terms

Accuracy

How often predictions are correct.

Precision

How many positive predictions were actually correct.

Recall

How many actual positives were correctly identified.

Bias

Systematic errors that affect fairness.

Conclusion

This glossary provides a foundation for understanding AI discussions. As you learn more, these concepts will become increasingly clear.

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Continue your AI learning journey.