flowchart TD
A["🤖 Artificial Intelligence\n(broad goal: intelligent behaviour)"]
B["📊 Machine Learning\n(learn from data)"]
C["🧠 Deep Learning\n(learn via neural networks)"]
D["💬 Natural Language Processing"]
E["👁️ Computer Visio"]
F["🎮 Robotics …"]
G["🔡 Large Language Models\n(ChatGPT, Gemini, claude...)"]
A --> B
B --> C
A --> D
A --> E
A --> F
C --> G
The AI Landscape
What is Artificial Intelligence?
Artificial Intelligence (AI) is the broad field concerned with building systems that can perform tasks normally requiring human intelligence — recognising images, understanding language, making decisions, or playing games. Example: Tax software that uses thousands of human-written “if-then” rules to calculate your deductions (an “Expert System”), but it isn’t learning anything new.
Machine Learning (ML) is a subset of AI. Instead of explicitly programming the rules, we give the machine data and let it learn the rules itself. Example: Netflix recommending movies based on your viewing history.
Deep Learning (DL) is a subset of ML that uses multi-layered artificial neural networks, inspired by the structure of the human brain. It thrives on massive amounts of unstructured data (like raw images, audio, or text). Example: Facial recognition on your smartphone or a chatbot.
A Brief Timeline
| Era | Milestone |
|---|---|
| 1950s | Turing Test; first neural network concepts |
| 1980s | Expert systems; backpropagation |
| 1990s | SVMs; decision trees mature |
| 2012 | Deep learning wins ImageNet — a turning point |
| 2017 | Transformer architecture introduced |
| 2020s | Large Language Models (GPT, Claude, Gemini…) |
Where AI is Used Today
- Healthcare: tumour detection, drug discovery
- Finance: fraud detection, algorithmic trading
- Transport: route optimisation, autonomous vehicles
- Language: translation, summarisation, coding assistants
- Science: protein folding (AlphaFold), climate modelling