AI vs ML vs Deep Learning: Know the Difference

 AI vs ML vs Deep Learning: Know the Difference

INTRODUCTION

You've seen and heard the words everywhere.

AI is going to transform the world.

Machine learning is the way of the future.

"Deep learning is revolutionizing technology."

Here's the issue, though: Most people use these terms synonymously. 

They use the word "AI" and literally mean "ML. A chatbot is referred to as "deep learning. 

It's not "when it's not what it seems, it's when it's not what it seems".

And honestly? It's confusing. Even techies get confused! 

them up.

The good news is the differences are easy to make. Once you understand 

With them you'll never be confused again.

During the following 7 minutes, you will discover:

What is AI, ML, and deep learning and their differences? The difference between AI, ML and deep learning.

Their relationship to one another (the hierarchy)

- Examples in the real world of each

Which one is truly important for YOUR job?

Looking for a resolution? Let's go.

What is Artificial Intelligence (AI)?

Artificial Intelligence: Any machine which are able to think, learn and act are called Artificial Intelligence. 

Solve problems and act like a human.

Imagine AI as the super-super general term. Can be used to make simple 

From chat bots to self-driving cars, to game playing AIs.

It's not something that's new. AI has been in existence for a long time now, 

1950s. They wanted to create machines that would be able to "intelligent" 

Things that are normally performed by human intelligence.

The thing is, however, that AI is very general. It contains anything that 

Provides "intelligence" to a machine.

EXAMPLES OF AI:

In 1997, Virtual reality programs (Gamers wearing the VR helmet) beat the other computer games.

Siri, Alexa, Google Assistant - (virtual assistants).

These are the systems that recommend movies to you on Netflix, or music to you on Spotify.

- Chatbots (ChatGPT , Claude)

- Ride-hail services (Uber, Lyft)

Fraud detection systems (Credit card companies)

There are some of these that utilize machine learning. Some don't. They're all "AI" 

Because they are all intelligent things that machines do.

If it's a machine doing something that it's good at, but that is something that humans are not good at, then it is using artificial intelligence. If it's a machine performing some operation that it's better at than a human, it's a type of AI. 

normally requires intelligence, it's AI.

Now, machine learning is a particular WAY of creating AI systems.

What is Machine Learning (ML)?

Machine Learning is a way of building Artificial Intelligence systems that learn from data of being programmed by people.

The main difference between Machine Learning and traditional Artificial Intelligence is that old Artificial Intelligence systems needed people to program every rule. For example people had to program "If this happens then do that. If something else happens then do this thing."

Machine Learning works differently. You give it data. It finds the patterns by itself. You do not need to program the rules because Machine Learning learns them on its own.

For instance traditional Artificial Intelligence would require you to code "If an email contains the words 'buy now' then mark it as spam." On the hand Machine Learning involves showing it a large number of emails like 10,000 and it learns to recognize patterns. It figures out what spam emails look like all by itself.

Machine Learning is a part of Artificial Intelligence. All Machine Learning is Artificial Intelligence. Not all Artificial Intelligence uses Machine Learning.

Here are some real world examples of Machine Learning:

- Netflix gives you recommendations based on what you have watched so it learns your taste from your viewing history

- The spam filter in Gmail learns from the emails you mark as spam

- Systems that approve loans learn who is likely to default and who will pay back the loan

- Medical diagnosis uses Machine Learning to find patterns in millions of X-rays

- Fraud detection uses Machine Learning to learn what transactions look suspicious

Machine Learning is a big deal because people cannot program every single rule. With Machine Learning you can handle a number of patterns because the system learns at a large scale.

This is why technology companies are so interested, in Machine Learning. It solves problems that traditional programming cannot.

Now deep learning is a type of Machine Learning.

 

What is Deep Learning?

Deep Learning is a kind of machine learning. It uses networks that have layers. This is why people call it Deep Learning.

So what is Deep Learning? Imagine you have a filter that has one layer. Deep Learning uses layers. Sometimes it even uses hundreds or thousands of layers.

Each layer in Deep Learning looks at information. Then passes it on to the next layer. This helps create systems that can find patterns that people cannot find.

Deep Learning is a part of Machine Learning. All Deep Learning is Machine Learning, but not all Machine Learning uses Deep Learning.

Here is a simple way to think about it:

Artificial Intelligence is a field. Machine Learning is a part of it.. Deep Learning is an even smaller part.

Here are some examples of Deep Learning:

* You can unlock your phone with your face

* Google Photos can find pictures of dogs

* You can have conversations with ChatGPT or Claude

* Teslas can drive themselves

* Doctors can use Deep Learning to find cancer in X-rays

* Computers can play games like AlphaGo

So why is Deep Learning so powerful? The networks in Deep Learning are like the brain. The many layers, in Deep Learning help it learn things.

Deep Learning is not perfect.

* Deep Learning needs a lot of information to work. It needs millions of examples.

* Deep Learning needs strong computers to work. It needs computers called GPUs.

* Deep Learning needs a lot of time to learn. It can take weeks or months to train Deep Learning.

* Deep Learning needs people who know what they are doing. It is hard to set up Deep Learning.

Not every problem needs Deep Learning. Most Machine Learning problems can be solved with methods.

The hierarchy is clear now:

AI

Machine Learning

Deep Learning

The Hierarchy: AI vs ML vs Deep Learning

The relationship is like this:

HIERARCHY:

┌─────────────────────────────┐

│   Artificial Intelligence                                             │  ← Broadest (covers everything)

│        (ALL smart machines)                                      │

│                                                                                  │

│  ┌──────────────────────┐            │

│  │  Machine Learning                               │            │  ← Subset of AI

│  │  (learn from data)                                 │            │

│  │                                                              │            │

│  │  ┌────────────────┐         │            │

│  │  │ Deep Learning                    │         │            │  ← Subset of ML

│  │  │ (neural nets)                        │         │            │

│  │  └────────────────┘         │           │

│  │                                                              │           │

│  └──────────────────────┘           │

│                                                                                  │

└─────────────────────────────┘

SCOPE:

Artificial Intelligence: This is the biggest group. It includes everything that's smart.

Machine Learning: This is smaller. It is about systems that learn from data.

Deep Learning: This is the smallest. It is about networks.

WHEN TO USE EACH TERM:

Use Artificial Intelligence when: You are talking about machines in general

Use Machine Learning when: You are talking about systems that learn from data

Use Deep Learning when: You are specifically talking about neural networks

For example you can say:

"Artificial Intelligence is changing our industry." (This is correct. It is a statement)

"We use Machine Learning to make recommendations." (This is specific. It is about learning from data)

"Our image recognition uses Deep Learning." (This is very specific. It is about networks)

This is important to understand. A lot of people say "Artificial Intelligence" when they really mean "Machine Learning." Many articles, about ChatGPT (which's actually a Deep Learning model) have headlines that say "Artificial Intelligence breakthrough."

Now lets look at real-world examples of each Artificial Intelligence, Machine Learning and Deep Learning.

Real-World Examples: Which is Which?

EXAMPLE 1: Chess-Playing Computer

Deep Blue beat Garry Kasparov in 1997. It was intelligent.

Did not use machine learning. Programmers manually coded chess

rules and strategy.

They did it because they could. Chess rules are easy to know.

 AI: Yes

ML: No

DL: No

EXAMPLE 2: Email Spam Filter

Gmail learns what is spam by looking at millions of emails.

It finds patterns like words and sender behavior.

It uses machine learning but not neural networks. Simple

machine learning works well for this.

AI: Yes

ML: Yes

DL: No

EXAMPLE 3: ChatGPT

ChatGPT understands your question thinks about context.

Gives relevant answers. This needs learning like a

transformer neural network.

It learned from billions of text examples. Neural networks

were needed for this.

AI: Yes

ML: Yes

DL: Yes

COMPARISON:

Deep Blue: AI with rules

Gmail: Machine learning finds patterns

ChatGPT: Deep learning with neural networks

The pattern is that as things get more complex:

Rules → Machine learning → Deep learning

The question is not which one is best.

It is which one is right, for this problem?

Now lets talk about your career.

Which One Should YOU Learn?

Different paths are better for people.

FOR NON- PEOPLE

It is enough to understand these terms. You do not need to learn them deeply. You can use this knowledge to understand news and have conversations about Artificial Intelligence. You can also use this knowledge to evaluate Artificial Intelligence claims. That is sufficient.

FOR BUSINESS PEOPLE

You should know the differences between Artificial Intelligence and Machine Learning and Deep Learning. Artificial Intelligence can help your business. You should understand what is possible with Artificial Intelligence. You do not need to know how to code. You should know which tools exist, such as Machine Learning based recommendations or Deep Learning based image recognition.

FOR DEVELOPERS

You should learn the basics of both Machine Learning and Deep Learning. You should understand algorithms. You should start building projects. You can use Python and TensorFlow or PyTorch to do this.

FOR DATA SCIENTISTS

You should learn a lot about Machine Learning. You should understand statistics and algorithms and data preparation. You will use Deep Learning sometimes. Most of your work will be with other Machine Learning.

FOR MACHINE LEARNING ENGINEERS AND ARTIFICIAL INTELLIGENCE RESEARCHERS

You should become very good at both Machine Learning and Deep Learning. You will specialize in one. You should understand both. This will take a time maybe two to five years. It is not something you can learn quickly.

KEY INSIGHT:

You do not need to learn about Artificial Intelligence and Machine Learning and Deep Learning deeply. You should focus on what's important, for your goals.

The bottom line is that Artificial Intelligence and Machine Learning and Deep Learning are different. They are related to each other.

MY RECOMMENDATION

WHAT YOU SHOULD DO NOW:

If you're confused about the terms → You got it! Move on.

If you want to learn more → Read my Day 1 post about ML basics.

If you're considering a career change → Study which interests you:

- AI broadly? Study all three.

- Machine learning specifically? Focus on algorithms.

- Deep learning specifically? Study neural networks.

If you want to build skills → Start with Python. Then:

- For ML: Learn Scikit-learn library

- For DL: Learn TensorFlow or PyTorch

The important thing? Don't get stuck in terminology. 

Move from "knowing about" → to "doing."

Real learning comes from building projects, not reading articles.


Read more  : Machine Learning  Basics