What Is Artificial Intelligence? Definition and Examples
Learn what artificial intelligence is, how it works, its main types, key milestones, everyday applications, benefits, and limitations.
Artificial Intelligence: The Foundations to Learn First
Start with what artificial intelligence means, how a model turns input into output, and where machine learning and deep learning fit. This cluster builds the vocabulary needed before exploring generative AI, agents, and analytics applications.
12 articles
A practical reading order
Four steps through the definition, mechanism, hierarchy, and methods behind AI systems.
- 01
what artificial intelligence means and where it falls short — Begin with the definition, a short history, everyday examples, and current limitations.
- 02
how AI moves from input to output — Follow the process through data preparation, training, inference, and post-processing.
- 03
the difference between AI, machine learning, and deep learning — Place the three terms in the correct hierarchy and understand when each applies.
- 04
AI algorithms and the problems they solve — Compare common methods by data type, objective, trade-off, and evaluation target.
Start here
Topic map
Core AI concepts
Start with the definition, operating process, relationship to machine learning, and common algorithms.
- How Does AI Work? From Training Data to Output
Understand how AI works through data preparation, model training, inference, and output, with clear examples and practical limitations.
- AI vs Machine Learning vs Deep Learning: Key Differences
Compare AI, machine learning, and deep learning by scope, methods, data requirements, computing needs, and practical use cases.
- 10 Popular AI Algorithms and When to Use Them
Explore 10 popular AI and machine learning algorithms, how they work, common use cases, trade-offs, and a practical selection process.
AI types and approaches
Distinguish systems by the scope of their abilities, the output they produce, and how they pursue a goal.
- What Is Narrow AI? How It Works and Everyday Examples
Narrow AI is designed for a specific task or bounded set of tasks. Learn how it works, where it appears, and how it differs from AGI.
- What Is Generative AI? How It Works and Real-World Uses
Generative AI creates text, images, audio, video, and code from learned patterns. Learn how it works, where it is used, and what can go wrong.
- What Is Agentic AI? How It Works and How It Differs
Agentic AI can plan and take actions towards a goal. Learn how it works, what components it needs, and how it differs from traditional AI.
AI platforms and models
Understand popular AI products through concise guides to their purpose, features, access, and limits.
- What Is Gemini AI? Features, Uses, and How to Get Started
Gemini is Google's AI assistant for writing, research, file analysis, images, and everyday tasks. Learn its main features and how to use it.
- What Is Meta AI? Features, Uses, and How to Access It
Meta AI is an assistant available across WhatsApp, Instagram, Facebook, Messenger, the web, and its own app. Learn what it does and how to use it.
- What Is Claude AI? Features, Uses, and How to Get Started
Claude is Anthropic's AI assistant for writing, document analysis, research, coding, and data work. Learn its main features and how to use it.
- What Is Perplexity AI? How It Works, Features, and Uses
Perplexity is an AI search engine that summarises information and links to its sources. Learn how it works, what it offers, and how to verify answers.
- What Is Grok AI? Features, Uses, and How to Access It
Grok is xAI's assistant for questions, web and X search, content, images, and coding. Learn its features, access, limitations, and privacy controls.
Related categories
- Generative AI & LLMs
Explore how generative models produce responses, retrieve external context, and still fail. The focus is technical enough to be useful without turning into model-development documentation.
- AI Agents & Intelligent Systems
AI agents do more than generate text. This cluster explains how models use tools, retain context, complete steps, and operate within limits that people can inspect and control.
- AI for Marketing Analytics
This cluster examines how AI connects data, detects changes, and builds analytical context. The emphasis is not automation promises, but decision material that remains open to verification.