Artificial Intelligence (AI) and ML

Artificial Intelligence

AI is the capability of a machine to mimic human intelligence and perform tasks that typically require human intelligence. This includes reasoning, discovering new information, and inferring data from various sources.

Core Concepts

• Enables a machine to simulate human behaviour
• Focused on maximising chances of success
• Includes learning, reasoning and self correction

Types of AI

• General AI (strong): Theoretical AI with human-level intelligence across domains
• Narrow AI (weak): AI designed for specific tasks or domains

Machine Learning

ML is a subset of AI that focuses on the ability of machines to learn from data and make predictions or decisions without being explicitly programmed.

Core Concepts

• Subset of AI which allows a machine to automatically learn from past data without programming explicitly
• Allow machines to learn from data so they can give accurate output
• Focused on accuracy and patterns
• Includes learning and self correction when introduced with new data

Types of ML

• Supervised learning
• Unsupervised learning
• Semi-supervised learning
• Reinforcement learning

Comparison: Artificial Intelligence vs Machine Learning

While Machine Learning is a subset of Artificial Intelligence, there are key differences in their scope, goals and approaches.

Core Capabilities

• AI: Enables a machine to simulate human behaviour
• ML: Subset of AI which allows a machine to automatically learn from past data without programming explicitly

Goals

• AI: Make a smart computer system to solve complex problems
• ML: Allow machines to learn from data so they can give accurate output

Focus Areas

• AI: Focused on maximising chances of success
• ML: Focused on accuracy and patterns

Approach

• AI: Includes learning, reasoning and self correction
• ML: Includes learning and self correction when introduced with new data