What's the difference between AI, ML, and DL?

AI (Artificial Intelligence) is the broad goal of making machines intelligent. ML (Machine Learning) is a method of achieving AI by learning from data. DL (Deep Learning) is a specific type of ML using neural networks with multiple layers.

What does "training" mean in AI?

Training is the process where an AI system learns from examples (data) to improve its performance. Like a student studying for an exam, the AI analyzes patterns in training data to make better predictions on new data.

What is a neural network in simple terms?

A neural network is a computer system inspired by the human brain. It consists of interconnected nodes (like brain neurons) that process information and learn patterns from data to make predictions or decisions.

What does "algorithm" mean in AI context?

An algorithm is a set of step-by-step instructions that tells the computer how to solve a problem or process data. In AI, algorithms define how the system learns from examples and makes predictions.

AI Terminology Explained: Beginner Glossary of Key Concepts

AI Terminology Guide: Essential Terms Explained Simply

Understanding AI terminology doesn't require a computer science degree. This beginner-friendly glossary explains the most important AI terms in simple language, helping you navigate conversations about artificial intelligence with confidence.

How to Use This Guide

Each term includes a simple definition, analogy, and real-world example. Terms are organized from basic concepts to more advanced topics, building your AI vocabulary step by step.

Core AI Concepts

Artificial Intelligence (AI)

Definition: Technology that enables machines to perform tasks that typically require human intelligence, such as recognizing speech, making decisions, or solving problems.

Simple Analogy: Like teaching a computer to "think" and solve problems the way humans do

Examples: Voice assistants (Siri), recommendation systems (Netflix), chatbots

Machine Learning (ML)

Definition: A method of achieving AI where computers learn to improve their performance on tasks by analyzing data and identifying patterns, without being explicitly programmed for every scenario.

Simple Analogy: Like a student learning from examples - the more examples they see, the better they get at recognizing patterns

Examples: Email spam detection, photo recognition, medical diagnosis

Deep Learning

Definition: A specialized type of machine learning that uses neural networks with multiple layers to process complex data like images, speech, and text.

Simple Analogy: Like having multiple levels of analysis - each layer examines different aspects of the data

Examples: Image recognition, language translation, autonomous driving

Neural Network

Definition: A computer system inspired by the human brain, consisting of interconnected nodes (neurons) that process information and learn from data.

Simple Analogy: Like a simplified version of brain neurons working together to process information

Examples: The foundation of ChatGPT, image recognition systems, voice assistants

Learning and Training Terms

Training Data

The examples used to teach an AI system. Like textbooks for students.

Example: Thousands of cat photos to teach AI to recognize cats

Supervised Learning

Learning with labeled examples and correct answers provided.

Example: Email labeled as "spam" or "not spam"

Unsupervised Learning

Finding patterns in data without being given correct answers.

Example: Grouping customers by shopping behavior

Reinforcement Learning

Learning through trial and error with rewards for good actions.

Example: AI learning to play chess by winning/losing games

Algorithm

Step-by-step instructions that tell the computer how to solve problems.

Example: Recipe-like instructions for recognizing faces

Model

The "brain" of the AI after training - what makes predictions.

Example: The trained system that recognizes spam emails

Modern AI Terms (LLMs & Generative AI)

Large Language Model (LLM)

Definition: AI systems trained on vast amounts of text to understand and generate human-like language.

Simple Analogy: Like a super-smart autocomplete that has read millions of books and can write like a human

Examples: ChatGPT, Claude, Gemini, GPT-4

Generative AI

Definition: AI that creates new content like text, images, music, or code rather than just analyzing existing data.

Simple Analogy: Like an artist that can create original paintings, stories, or songs based on what it learned

Examples: ChatGPT (text), DALL-E (images), Midjourney (art), GitHub Copilot (code)

Prompt

The instructions or questions you give to an AI system.

Example: "Write a short story about a robot learning to paint"

Fine-tuning

Adjusting a pre-trained AI model for specific tasks or behaviors.

Example: Training ChatGPT to be more helpful and harmless

Performance and Evaluation Terms

Accuracy

How often the AI gets the right answer.

Example: 95% accuracy = correct 95 out of 100 times

Bias

When AI makes unfair or prejudiced decisions based on training data.

Example: Hiring AI that favors certain demographics

Overfitting

When AI memorizes training examples but can't handle new situations.

Example: Student who memorizes textbook but fails on new problems

Validation

Testing AI on new data to ensure it works beyond training examples.

Example: Testing on photos the AI has never seen before

Inference

When trained AI makes predictions or decisions on new data.

Example: Using trained model to identify objects in new photos

Hallucination

When AI generates false or nonsensical information confidently.

Example: ChatGPT making up fake historical facts

Types of AI Systems

Narrow AI (Weak AI)

AI designed for specific tasks only.

Current AI: Chess AI, image recognition, language translation

General AI (Strong AI)

Hypothetical AI with human-level intelligence across all domains.

Future concept: AI that matches human cognitive abilities

Superintelligence

Theoretical AI that surpasses human intelligence in all areas.

Speculative: AI more capable than humans at everything

Technical Terms Made Simple

API

A way for different software programs to communicate and share data.

Cloud Computing

Using powerful computers over the internet instead of your own device.

GPU

Specialized computer chips that are excellent at AI calculations.

Open Source

AI models or software that anyone can access, modify, and use freely.

Transformer

A type of neural network architecture especially good at understanding language.

Token

Small pieces of text (words or parts of words) that AI processes.

Quick Reference Guide

When Someone Says...

"The model" = The trained AI system
"Training data" = Examples used to teach AI
"Inference" = AI making predictions
"Fine-tuning" = Customizing AI for specific tasks
"Prompt" = Instructions you give to AI

They Mean...

The AI "brain" that makes decisions
The learning materials for AI
AI using what it learned on new problems
Teaching AI to be better at specific jobs
Your questions or requests to AI

Beginner Guide to AI Terminology: Key Terms You Should Know