AI, ML, and DL: A Nested Analogy

Recommended resources

The freely available resources this lesson will direct you to.

Welcome to your next lesson. In our previous session, we established a crucial framework for thinking about AI by categorizing it based on its capability. We distinguished between the specialized Narrow AI of today and the hypothetical, human-like General AI of the future. This gives us the "what."

Today, we'll add another essential layer to your understanding by categorizing AI based on its methodology. You've likely heard the terms Artificial Intelligence, Machine Learning, and Deep Learning used in articles and conversations, often interchangeably. This lesson will untangle that confusion. We will define precisely what each term means and how they relate to one another, using a simple visual analogy that is key to AI literacy. Mastering this hierarchy will allow you to understand not just what an AI system does, but, at a high level, how it works.

The Russian Doll Analogy

The most important thing to know is that AI, Machine Learning (ML), and Deep Learning (DL) are not separate, competing concepts. Instead, they have a nested relationship, like a set of Russian Matryoshka dolls.

Artificial Intelligence (AI) is the largest doll, the outer shell. It is the entire, broad field of making machines intelligent.
Machine Learning (ML) is the next doll inside. It is a specific subset of AI, representing a particular approach to creating intelligence.
Deep Learning (DL) is an even smaller doll inside ML. It is a highly specialized technique within Machine Learning that has powered most of the recent breakthroughs in AI.

The image below provides a great visual summary of this hierarchy. It also includes "Generative AI," a term you're familiar with through tools like ChatGPT, placing it correctly as a subset of Deep Learning.

This diagram illustrates the nested relationship: AI is the broadest field, containing Machine Learning, which in turn contains Deep Learning, which itself contains Generative AI.

Let's break down each of these layers, starting from the outside and working our way in. The following short video from IBM Technology provides an excellent and concise overview of this hierarchy.

Machine Learning vs. Deep Learning vs. Foundation Models

This video clearly defines AI, ML, and DL, explaining how each is a subset of the previous one. It's a perfect primer before we dig into the details.

Please watch the following segments: The definition of AI as the broadest field. The explanation of Machine Learning as a subfield of AI that learns from data. The introduction to Deep Learning as a subset of Machine Learning using neural networks.

Now that you have the high-level overview from the video, let's solidify these concepts with a more detailed reading.

A Deeper Look at the Layers

The following article from the Prompt Engineering Institute uses the "Matryoshka doll" analogy and provides precise definitions for each layer. It's a great resource for building a technically accurate, yet still conceptual, understanding.

The Core Technical Hierarchy: Deconstructing AI, Machine Learning ...

This article explains the nested hierarchy of AI, ML, and DL, emphasizing the shift from rule-based systems to data-driven learning. It's key to understanding why machine learning became the dominant approach.

As you read, focus on how each layer represents a refinement of the one before it. Start with the Introduction to get a sense of the "nested hierarchy" concept. Next, read the section on Artificial Intelligence. Notice how it's defined as the overarching field that simulates human intelligence processes. Then, read about the paradigm shift to Machine Learning. The key idea here is moving from humans writing the rules to machines learning the rules from data. Finally, read the section on Deep Learning. Pay attention to how its multi-layered "deep" architecture is what allows it to handle complex data like images and text.

From Concepts to Concrete Examples

To make sure these definitions are not just abstract, let's connect them to real-world applications. The distinction between these layers becomes very clear when you look at the kinds of problems they solve.

1. Artificial Intelligence (AI): The Big Idea

Definition: The broadest term, referring to any system that performs a task requiring human-like intelligence.
Example: A self-driving car. This is a complex AI system. It's not just one program; it's an integration of many different systems. Some might be rule-based (e.g., "IF speed limit is 50 mph, THEN do not exceed 50 mph"), while others, like identifying a pedestrian, use Machine Learning. The entire car's behavior is considered AI.

2. Machine Learning (ML): Learning from Experience

Definition: A subset of AI where the system learns to perform a task by finding patterns in data, rather than being explicitly programmed.
Example: Your email spam filter. In the past, engineers would have tried to write rules like "IF email contains 'free money', THEN it is spam." This is brittle and easy for spammers to get around. A modern spam filter uses ML. It is "trained" on millions of emails that you and other users have marked as spam. It learns the subtle patterns—combinations of words, sender reputation, email structure—that predict a spam email, and it gets better over time as it sees more data.

3. Deep Learning (DL): The Modern Powerhouse

Definition: A subset of ML that uses complex, multi-layered "neural networks" to learn intricate patterns directly from raw data like images, sound, or text.
Example: Facial recognition on your phone or in social media photo tagging. Before Deep Learning, a programmer would have to try to define what a "face" looks like by coding features like "an oval shape containing two circles and a line." This is nearly impossible to do well. A Deep Learning model is simply shown millions of photos with faces and millions without. Its deep layers automatically learn to identify the hierarchical features—from simple pixels to edges, then to shapes like eyes and noses, and finally to a complete face. This ability to learn features automatically is what makes Deep Learning so powerful and is the engine behind tools like ChatGPT and modern image generators.

The article below from All Things Open provides another great set of real-world examples that reinforce these distinctions.

AI vs ML vs DL: A practical guide with real-world ...

This article offers clear, practical examples for each term, helping to ground the concepts in things you might encounter every day.

Please read the following three short sections: The section on AI, which uses the self-driving car example. The section on Machine Learning, using the spam filter example. The section on Deep Learning, using image recognition.

Conclusion

In this lesson, we've organized the complex world of AI into a clear, hierarchical structure. By understanding this framework, you've taken a significant step toward AI literacy.

Here are the key takeaways:

AI, ML, and DL are not synonyms. They represent a nested hierarchy, like Russian dolls, where DL is a type of ML, and ML is a type of AI.
Artificial Intelligence (AI) is the entire field of creating intelligent machines.
Machine Learning (ML) is an approach to AI that focuses on systems that learn patterns from data, rather than being programmed with explicit rules.
Deep Learning (DL) is a powerful type of Machine Learning that uses deep neural networks to learn complex patterns from vast amounts of raw data. It is the technology behind most of the recent "AI explosion."

Now, when you read a news article or hear a discussion about AI, you can mentally slot these terms into their proper place. You understand that when people talk about the latest amazing AI, they are almost always talking about a breakthrough in Deep Learning.

In our next lesson, we will address some of the most common myths and misconceptions about AI. Your new understanding of this AI/ML/DL hierarchy will be the perfect tool to help you see why ideas like "AI thinking like a human" are more complicated than they seem.