Introduction to AI: A Map of the Territory

Artificial intelligence — AI for short — is one of the most talked-about technologies of our time. Yet for most people, it remains a fuzzy idea, somewhere between a smartphone assistant and a science-fiction robot. This course is about cutting through that fog and understanding AI as it actually exists today: a set of practical tools that are already shaping how we work, communicate, learn, and create.

What Is AI, Really?

At its core, AI refers to computer systems that perform tasks which, until recently, required human intelligence. These tasks include understanding language, recognising objects in photos, translating between languages, writing code, and generating images. Modern AI does not "think" the way humans do — it finds patterns in enormous amounts of data and uses those patterns to produce useful outputs.

Today's AI is often described as narrow AI: systems that are very good at one specific kind of task but cannot generalise beyond it. A system that translates Spanish to English is not also able to drive a car. This is very different from the general, all-purpose AI of science fiction — and this course focuses entirely on the real thing.

How AI Learns

Unlike traditional software, which follows rules written by a programmer, modern AI systems learn from examples. Show a system millions of photographs labelled "cat" or "not cat," and it learns to recognise cats. Feed it billions of sentences from the internet, and it learns to generate fluent, coherent text. This learning-from-data approach, known broadly as machine learning, is what makes modern AI so versatile — and also what gives it its quirks and blind spots.

The Major Technologies You Will Encounter

This course covers the AI tools that are most present in everyday life right now:

• Large Language Models (LLMs) such as ChatGPT and Claude — systems trained on vast quantities of text that can write, summarise, translate, answer questions, and hold conversations.
• AI Image Generation tools such as DALL·E and Midjourney — systems that create or edit images from a text description.
• AI Translation services such as DeepL and Google Translate — systems that convert text or speech between languages with remarkable fluency.
• AI Code Generation tools such as GitHub Copilot — systems that suggest or write programming code based on a plain-language description of what you want.

Each of these builds on the same core idea — learning from large amounts of data — but applies it to a different domain.

The four major AI application areas covered in this course, all rooted in the same underlying idea of learning from data.

The Practical and Societal Side

AI is not just a technical subject — it is a social one. This course pays serious attention to the real-world implications of AI:

• Strengths and limitations: AI can be remarkably capable and surprisingly unreliable at the same time. Understanding when to trust it — and when not to — is a core skill.
• Bias: Because AI learns from human-generated data, it can absorb and amplify human prejudices. Recognising this is essential for using AI responsibly.
• Privacy: Many AI systems are trained on or interact with personal data, raising questions about who owns that data and how it is used.
• Safety: As AI becomes more capable and widely deployed, questions about misuse, misinformation, and unintended consequences become increasingly important.

What This Course Will Give You

By the end of this course, you will have a clear mental model of how today's AI systems actually work, enough to use them intelligently and critically. You will not need to write code or solve equations. Instead, you will develop the conceptual vocabulary to understand what AI can and cannot do, where it comes from, and how to engage with it — as a user, a citizen, and a thoughtful member of a society being shaped by these tools.