1.1.1. 💡 First Principle: AI, ML, and Deep Learning

First Principle: Artificial Intelligence (AI) is the broad concept of creating intelligent machines; Machine Learning (ML) is a subset of AI where machines learn from data; and Deep Learning (DL) is a specialized type of ML that uses multi-layered neural networks.

Understanding the relationship between these terms is the starting point for any discussion about AI.

• Artificial Intelligence (AI): The broadest field, encompassing any technique that enables computers to mimic human intelligence. This can include everything from rule-based systems to advanced robotics.
• Machine Learning (ML): A subfield of AI. Instead of being explicitly programmed with rules, an ML system is trained on large amounts of data to learn patterns. This allows it to make predictions or decisions on new, unseen data.
• Deep Learning (DL): A subfield of ML. Deep learning uses complex, multi-layered "deep" neural networks inspired by the human brain. DL is particularly powerful for recognizing complex patterns in unstructured data like images, sound, and text, and it powers many of the most advanced AI applications today.

Scenario: Your company is using a system that identifies objects in images. You need to explain to a non-technical stakeholder how this technology fits into the broader landscape of AI.

Reflection Question: How would you describe the relationship between AI, ML, and DL to someone new to the field, using an analogy like Russian nesting dolls?

💡 Tip: Think of it as a hierarchy:

• AI (Artificial Intelligence) - The broad field
  • ML (Machine Learning) - A subset of AI that learns from data
    • DL (Deep Learning) - A subset of ML using deep neural networks