Machine Learning (ML) is a subset of artificial intelligence (AI) that focuses on enabling computers to learn from data and improve their performance on tasks without being explicitly programmed. Instead of following pre-defined instructions, machine learning systems use algorithms to identify patterns, make predictions, and adapt their behavior based on new information.

Machine Learning (ML) is a core subset of Artificial Intelligence (AI). While AI encompasses the broader goal of creating systems that simulate human intelligence, ML focuses on one specific way to achieve this—through learning from data.

Artificial Intelligence (AI) is indeed transforming the nature of work, but its impact is complex—it both replaces some jobs and creates new opportunities. The extent of its impact varies across industries, skill levels, and job roles.

Types of AI Based on Functionality:

1. Reactive Machines:

Definition: AI systems that react to current scenarios without storing past experiences.

Characteristics:

• No memory or learning ability.
• Focuses solely on immediate tasks.

Examples: IBM’s Deep Blue, which played chess against Garry Kasparov.

2. Limited Memory:

Definition: AI systems that use past data to inform current decisions.

Characteristics:

• Can learn from historical data.
• Common in most AI systems today.

Examples: Autonomous vehicles using sensor data to avoid collisions.

3. Theory of Mind:

Definition: AI that understands emotions, beliefs, and intentions to interact socially.

Characteristics:

• Still in the research phase.
• Requires understanding and processing human emotions.

Examples: Advanced virtual assistants capable of emotional responses (future).

4. Self-Aware AI:

Definition: AI with self-consciousness and awareness of its own existence.

Characteristics:

• Theoretical and highly speculative.
• Would possess emotions and understanding similar to humans.

Examples: AI depicted in sci-fi, like Ava in Ex Machina.