We ❤️ Open Source
A community education resource
AI vs ML vs DL: A practical guide with real-world engineering examples
From spam filters to self-driving cars, how AI actually works in practice.
Understanding the relationship between artificial intelligence (AI), machine learning (ML), and deep learning (DL) can be confusing. These terms are often used interchangeably, but they represent distinct concepts with specific applications. Let’s break down each term with practical examples that will help you grasp these technologies and how they’re shaping our world.
What is artificial intelligence (AI)?
Artificial intelligence is the broadest concept of the three. It refers to creating machines or systems that can perform tasks typically requiring human intelligence. Think of AI as the umbrella term that encompasses all intelligent machine behavior.
Real-world artificial intelligence example
Consider a self-driving car navigating through busy city traffic. It makes split-second decisions like a human driver would, detecting pedestrians crossing the street, choosing when to change lanes safely and adjusting speed based on traffic conditions. This intelligent decision-making capability is what we call artificial intelligence. AI systems can reason, solve problems, perceive their environment and even understand language and tasks that once required human cognition.
Read more: Want to get into AI? Start with this.
What is machine learning (ML)?
Machine learning is a subset of AI that focuses on developing algorithms enabling machines to learn from data and make predictions or decisions without being explicitly programmed for every scenario.
Real-world machine learning example
Your email spam filter is a perfect example of machine learning in action. Instead of having programmers write rules for every possible spam email, the system learns from your interactions. When you mark emails as spam or move them to your inbox, the filter learns to recognize patterns in email content, sender behavior, and other factors. Over time, it becomes increasingly accurate at filtering spam automatically.
Understanding algorithms in machine learning
In the context of machine learning, an algorithm is a specific set of rules, mathematical equations, or procedures that the model follows to learn from data and make predictions. These algorithms process information, identify patterns, and continuously improve their performance as they encounter more data.
What is deep learning (DL)?
Deep learning is a specialized subfield of machine learning that uses neural networks with many layers (called deep neural networks) to learn and interpret complex patterns in data.
Real-world deep learning example
Image recognition software that can identify cats in photos across the internet uses deep learning. Unlike simpler ML algorithms, deep learning can automatically extract features from raw pixel data, learning to recognize the distinctive characteristics of cats whiskers, pointed ears, fur patterns without being explicitly told what makes a cat a cat.
Deep learning excels at handling unstructured data like images, audio, and text making it the driving force behind many modern AI breakthroughs.
Read more: Deep dive into the Model Context Protocol
Types of machine learning
A deeper dive into machine learning encompasses several distinct approaches, each suited for different types of problems and data.
Supervised learning: Learning from labeled data
Supervised learning involves training algorithms on labeled data, where the correct answer (or label) is provided for each example. The algorithm learns to map inputs to outputs based on these examples.
Credit card approval: A practical example
Traditional credit card approval processes were often slow and manual. Companies either relied on human reviewers or rules engines where skilled professionals built and maintained complex decision rules. This approach had significant drawbacks:
- Slow processing times (10–15 days or more)
- Need for skilled personnel to build and update rules
- Constantly changing rules requiring continuous updates
- However, it provided businesses with clear insight into decision-making processes
The machine learning solution
What if we could build rules by analyzing past data instead? This is where supervised machine learning shines. We all learn by examples, and past data is essentially a collection of examples.
By reviewing historical credit card approval decisions applications that were approved or denied along with the reasons, we can train a model to make similar decisions. Through a process called training, an algorithm incrementally updates a model by examining data samples one by one.
The algorithm learns patterns that distinguish approved applications from rejected ones factors like credit score, income, employment history, and debt-to-income ratio. Once trained, this model can predict whether to approve new credit card applications almost instantly, dramatically speeding up the process while maintaining or even improving accuracy. This is supervised machine learning: learning from labeled data where the outcome (approval or denial) is known for historical examples.
Unsupervised learning: Discovering hidden patterns
Unlike supervised learning, unsupervised learning works with data that doesn’t have specific outcomes or labels. The goal is to discover trends, patterns, and structures within the data that can provide valuable insights.
Retail marketing and customer segmentation
A retail company might collect customer information including household size, income, location, and occupation. Using unsupervised learning, the company can identify natural clusters in this data without being told what to look for. The algorithm might discover customer segments like:
- “Small families with high disposable income”
- “Budget-conscious large households”
- “Urban professionals with premium preferences”
These insights enable targeted marketing campaigns and personalized product recommendations.
Streaming service optimization
Streaming platforms collect data about viewing sessions, minutes watched per session, and the variety of shows consumed. Unsupervised learning can cluster users based on viewing behavior, helping the service:
- Recommend relevant content
- Optimize streaming quality based on usage patterns
- Identify potential subscription cancellation risks
Nutritional clustering example
Consider the nutritional content of fruits and vegetables. We know they contain different vitamins, minerals, and nutrients, but which ones are nutritionally similar? By applying unsupervised learning to nutritional data, we can cluster fruits and vegetables into groups with similar nutritional profiles. This helps people:
- Include nutritionally diverse foods in their diet
- Find substitutes for foods they don’t enjoy
- Plan balanced meals more effectively
Unsupervised learning excels at exploring patterns in data and grouping similar items into meaningful clusters all without predefined categories.
Reinforcement learning: Learning through trial and error
Reinforcement learning is perhaps the most intuitive form of machine learning because it mirrors how humans learn many skills.
Learning chess: A human analogy
Think about learning to play chess:
- You make a move (take an action)
- You observe whether it was effective (receive feedback)
- You remember the outcome for future decisions (learn)
Reinforcement learning follows this same pattern. A computer program learns to make decisions by trying different actions and receiving feedback in the form of rewards or penalties.
Real-world applications
Autonomous vehicle driving: Self-driving cars use reinforcement learning to navigate complex traffic situations. The system:
- Takes actions (steering, braking, accelerating)
- Receives rewards for safe, efficient driving
- Gets penalties for violations or dangerous behavior
- Continuously improves its driving strategy
A note on robotics: Robots learn to perform tasks like grasping objects, walking, or assembling products through thousands of practice attempts, refining their movements based on success and failure.
Deep learning: Automatic feature extraction
Deep learning represents a powerful evolution in machine learning, addressing a fundamental challenge: automatically extracting meaningful features and rules from raw data.
The challenge of manual feature engineering
Can you identify whether an image contains a cat or dog by looking at just one pixel? Can you write explicit rules to identify cats versus dogs in any image? These questions highlight why traditional approaches struggle with complex data.
Manually defining features (like “pointy ears” or “wet nose”) is:
- Time-consuming and requires domain expertise
- Limited by human perception
- Difficult to scale across different types of data
How deep learning solves this
Deep learning uses neural networks with multiple layers to automatically learn features from raw data like pixels. These networks can:
- Identify low-level features (edges, colors) in early layers
- Combine them into mid-level features (textures, shapes) in middle layers
- Recognize high-level concepts (cat faces, dog breeds) in deeper layers This hierarchical learning happens automatically through training, without humans having to specify what features matter.
Understanding neural networks: The foundation of deep learning
Neural networks are the computational architecture that powers deep learning. They’re inspired by the structure of the human brain, consisting of interconnected layers of artificial neurons.
How neural networks work
Neural networks are essentially stacked layers of computational units (neurons) that process information. Each layer transforms its input in some way, passing the result to the next layer.
In the context of machine learning, neural networks are a form of supervised learning particularly effective at function approximation — estimating complex relationships between inputs and outputs by examining patterns in data.
For example, a neural network can learn the hidden function that maps pixel values to the concept “cat” or “dog” by training on thousands of labeled images.
Why layers matter
The “deep” in deep learning refers to using many layers in neural networks. More layers allow the network to learn increasingly abstract and sophisticated representations of data, enabling it to tackle more complex problems like natural language understanding and realistic image generation.
Generative AI: Creating new content
Generative AI represents one of the most exciting applications of machine learning, particularly deep learning. It’s a subset of AI focused on creating new content text, images, audio, video and more.
How generative AI works
Generative models learn patterns from existing data and use that understanding to craft fresh, creative output. Instead of just classifying or predicting, these models generate entirely new content that didn’t exist before.
Example: ChatGPT
ChatGPT and similar language models are powered by deep neural networks trained on vast amounts of text data. They learn:
- Grammar and syntax patterns
- Contextual word relationships
- Common knowledge and reasoning patterns
- Writing styles and tones
When you ask ChatGPT a question, it generates text-based responses by predicting sequences of words that would naturally follow your query, based on the patterns it learned during training.
Applications of generative AI
Generative AI is transforming multiple industries:
- Content creation: Writing articles, generating marketing copy, creating stories
- Art and design: Producing images, designing logos, creating concept art
- Music composition: Composing original music in various styles
- Code generation: Writing and debugging software code
- Drug discovery: Designing new molecular structures for pharmaceuticals
The relationship: How it all fits together
To visualize the relationship between these concepts, imagine a set of nested circles:
- Artificial intelligence (largest circle) encompasses all intelligent machine behavior.
- Machine learning (middle circle) is a subset of AI focused on learning from data.
- Deep learning (smallest circle) is a subset of ML using deep neural networks.
- Generative AI sits within machine learning and deep learning, representing models that create new content.
Each layer builds upon the previous one, with increasing specialization and capability.
Practical implications for businesses and individuals
Understanding these distinctions helps:
| Businesses | Individuals |
|
|
Conclusion: The future of intelligent systems
The future of intelligent systems such as artificial intelligence, machine learning, and deep learning represent a hierarchy of increasingly sophisticated approaches to creating intelligent systems. From the broad vision of AI to the specific techniques of deep learning and the creative power of generative AI, these technologies are reshaping how we solve problems and interact with machines. As these fields continue to evolve, we’ll see even more powerful applications emerge from more accurate medical diagnoses to more efficient energy systems to more personalized education. Understanding the fundamentals today positions you to participate in and benefit from the AI revolution tomorrow.
More from We Love Open Source
- Want to get into AI? Start with this.
- Deep dive into the Model Context Protocol
- What is artificial intelligence (AI) and the three things it does well
- What is machine learning and how does AI actually learn?
- What is deep learning and how does it work?
The opinions expressed on this website are those of each author, not of the author's employer or All Things Open/We Love Open Source.