Learning path: AI basics

The concepts to learn before AI basics, in order. Each step links to its page and, where one exists, its quiz.

Your path

ComputersDirect prerequisite
A computer is a device that takes in digital data, processes it by following a program of instructions, stores it and produces results. Inside, everything is represented as bits, each either 0 or 1. Most computers still follow the organisation John von Neumann described in 1945: a processing unit, a control unit, a memory that holds both instructions and data, and input and output.
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AI basicsYour goal
Artificial intelligence (AI) is the field of building machine-based systems that, for a given set of objectives, produce outputs such as predictions, recommendations or decisions. Early AI tried to write intelligence down as hand-made rules; much of modern AI instead learns patterns from data. Today's systems can be remarkably strong at some tasks and surprisingly weak at others, so knowing what an AI system was built and tested to do matters as much as knowing what it can do.
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Concepts that build directly on AI basics:

AI agents: An AI agent is a system that perceives its environment, decides what to do and takes actions towards a goal, repeating that loop rather than giving a single answer. Today's best-known agents wrap a language model in a loop with memory, planning and tools — search, calculators, code, a computer's mouse and keyboard — so it can carry out multi-step tasks. Agents have improved quickly on benchmarks (as of the AI Index 2026) but still fail often, and acting in the world raises new risks such as compounding errors and prompt injection, so their autonomy needs limits and human oversight.
Machine learning: Machine learning is the part of AI in which a program improves at a task through experience — data — instead of following rules written in advance. Supervised learning learns from labelled examples, unsupervised learning finds structure in unlabelled data, and reinforcement learning learns which actions earn reward by trial and error. The real test of a learned model is not how well it fits its training data but how well it performs on new data it has never seen.