Major classes of problems that AI can address

Classification

Description: Assigning items to predefined categories based on their features.
Examples: Image recognition, spam detection, sentiment analysis.

Regression

Description: Predicting a continuous value based on input features.
Examples: Stock price prediction, weather forecasting, demand estimation.

Clustering

Description: Grouping similar items together without predefined labels.
Examples: Customer segmentation, image compression, anomaly detection.

Optimization

Description: Finding the best solution from a set of possible solutions, often under constraints.
Examples: Route optimization, resource allocation, scheduling problems.

Pattern Detection

Description: Identifying regularities and trends in data.
Examples: Fraud detection, market basket analysis, signal processing.

Anomaly Detection

Description: Identifying outliers or unusual patterns that deviate from the norm.
Examples: Intrusion detection, equipment failure prediction, quality control.

Language Cognition

Description: Understanding and generating human language.
Examples: Natural language processing (NLP), sentiment analysis, machine translation, chatbots.

Vision

Description: Processing and interpreting visual information from the world.
Examples: Object detection, facial recognition, image segmentation.

Knowledge Representation

Description: Storing and organizing information in a way that a computer system can use to solve complex tasks.
Examples: Semantic web, ontologies, expert systems.