AI v ML

Think of AI as the big umbrella goal (creating smart machines) and ML as a key tool (learning from data) to achieve that goal, alongside other AI methods like rule-based systems or NLP. 

Artificial Intelligence (AI) is the broad concept of machines mimicking human intelligence, while Machine Learning (ML) is a specific subset of AI that enables systems to learn from data to find patterns and make predictions without explicit programming, using algorithms to improve performance over time. 

Think of AI as the big umbrella goal (creating smart machines) and ML as a key tool (learning from data) to achieve that goal, alongside other AI methods like rule-based systems or NLP. 

This video provides a brief overview of the differences between AI and ML:

Artificial Intelligence (AI)

• Goal: To create systems that can perform tasks requiring human-like intelligence (reasoning, problem-solving, perception).
• Scope: Very broad; includes ML, Deep Learning, NLP, robotics, expert systems, etc..
• Methods: Can use ML, but also logic, rules, or search algorithms (like in video games).
• Examples: Self-driving cars, virtual assistants (Siri, Alexa), complex planning systems. 

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