Subfields of Artificial Intelligence

Below are key subfields of AI, along with how they work and real-world examples:

Machine Learning (ML)

Teaches computers to learn from data. Algorithms identify patterns from historical data to make predictions or decisions without explicit programming.

• Example: Spam filters learn from email patterns to detect junk messages.

Deep Learning

A specialized subset of ML using artificial neural networks with many layers (deep networks) to learn complex patterns. Commonly used in vision, speech, and language tasks.

• Example: Image recognition software that distinguishes cats from dogs.

Natural Language Processing (NLP)

Enables machines to understand, interpret, and generate human language. NLP powers translation tools, chatbots, and voice assistants.

• Example: Google Translate uses NLP to convert text between languages.

Generative AI

Produces original content based on patterns learned from data. It generates text, images, audio, and even code.

• Example: ChatGPT generates essays; DALL·E creates images from text prompts.

Robotics

Integrates AI with mechanical systems to enable automated physical tasks. Robots perceive surroundings, make decisions, and perform actions.

• Example: Autonomous warehouse robots that sort and move packages.