Machine Learning vs AI

People use "AI" and "Machine Learning" interchangeably. They're related, but not the same thing.

The Relationship

Think of it like this:

Artificial Intelligence (the big umbrella)
    └── Machine Learning (a technique to achieve AI)
            └── Deep Learning (a type of ML)
                    └── LLMs like GPT (a type of deep learning)

AI is the goal: Make computers do intelligent things. Machine Learning is one way to get there: Let computers learn from data.

The Traditional Approach (Pre-ML)

Early AI used explicit rules programmed by humans:

IF email contains "Nigerian prince" AND "send money"
THEN mark as spam

The problem: You need to anticipate every scenario. Miss one trick, and spam gets through.

The Machine Learning Approach

Instead of writing rules, you show the computer examples:

1. Feed it 100,000 emails labeled "spam" or "not spam"
2. The algorithm finds patterns you never thought of
3. It can catch spam tricks humans haven't even seen yet

The magic: The computer writes its own rules based on data.

Types of Machine Learning

Supervised Learning

• You provide labeled examples: "This is a cat, this is a dog"
• The model learns the mapping between input and label
• Use case: Email classification, price prediction

Unsupervised Learning

• You provide data without labels
• The model finds patterns on its own
• Use case: Customer segmentation, anomaly detection

Reinforcement Learning

• The model learns by trial and error
• Gets rewards for good actions, penalties for bad ones
• Use case: Game playing, robotics

Deep Learning: ML on Steroids

Deep Learning uses neural networks with many layers (hence "deep").

Why it matters:

• Can learn incredibly complex patterns
• Powers most modern AI (GPT, image generators, voice assistants)
• Needs lots of data and computing power

A Practical Example

Task: Identify dogs in photos

Traditional Programming:

IF has four legs AND has tail AND has fur AND...

Good luck defining every possible dog pose and breed.

Machine Learning:

Here are 1 million dog photos.
Here are 1 million non-dog photos.
Figure out the difference.

The model learns features humans can't even articulate.

Why This Matters for You

When people say "AI," they usually mean machine learning systems today. Understanding this helps you:

• Cut through hype: Not everything needs AI
• Ask better questions: "What data was this trained on?"
• Spot limitations: ML is only as good as its training data

The Bottom Line

• AI = The dream (intelligent machines)
• Machine Learning = The method (learning from data)
• Deep Learning = The technique powering today's breakthroughs

All modern AI you interact with—ChatGPT, image generators, recommendation systems—is machine learning under the hood.

Next up: How LLMs Work — The technology behind ChatGPT