Definition
AI divides into fields according to the kind of problem. Natural language processing handles text, computer vision handles images, speech AI handles audio, robotics acts in the physical world, recommender systems rank items, and planning and search choose sequences of actions.
In simple words
Think of a human: reading is NLP, seeing is vision, hearing is speech, moving is robotics, and deciding what to do next is planning. AI has a field for each.
Why it matters
It's your map of this course. Most jobs and projects sit in one or two fields, so you can choose where to go deep.
How it works
| Field | Input | Example | Module |
|---|---|---|---|
| NLP | Text | Translation, sentiment | Natural Language Processing |
| Computer vision | Images, video | Detection, OCR | Computer Vision |
| Speech | Audio | Voice typing, text-to-speech | Speech and Audio AI |
| Robotics | Sensors | Warehouse robots | Robotics |
| Recommenders | Behaviour | Product suggestions | Applied and Advanced ML |
| Planning, search | Goals, states | Routes, schedules | Agents and Search |
| Generative AI | Prompts | Writing, images | Large Language Models |
Try it yourself
AI's fields, like a person's senses
A home robot makes you tea. Each ability it uses is its own field of AI.
Which domain is it?
Look at what each system takes in: numbers and records, pictures, or human language.
Suggesting a film from what you've watched
Predicting tomorrow's sales from past sales
Deciding whether a review is positive
Reading the text from a scanned page
Translating Hindi into English
Finding the lanes on a road from a car camera
Common mistakes
- Trying to learn every field at once. Learn the common base, then choose one.
- Thinking these are separate worlds. They share maths and methods.
Check yourself
Summary
- Fields follow the type of problem and input.
- Learn the shared base first, then pick a field.
- Multimodal models are blurring the borders.
Finished reading?