Artificial Intelligence & Machine Learning: Know the Difference

Let’s be honest: if you’ve ever nodded along in a meeting where someone mentions Artificial Intelligence, Machine Learning, or Deep Learning—you’re not alone. These terms are used interchangeably, and the confusion is completely understandable.

So, let’s cut through it.

This article will help you understand the key differences between AI, Machine Learning, and related terms like Neural Networks and Deep Learning. We’ll also show you why it matters—especially in a modern manufacturing context.

And to make it even easier, we’ll be peeling back the layers of this handy visual framework (yes, it’s shaped like an onion). Each layer brings us closer to the most powerful applications of data in your operation.

Artificial Intelligence (AI) is the broadest layer. It refers to any technique that enables machines to simulate human intelligence. That includes reasoning, decision-making, learning, perception, and even creativity.

Examples of AI you might already know:

AI is the umbrella under which more intelligent, faster, and more autonomous systems are built in manufacturing.

Machine Learning (ML) is a subset of AI. It gives systems the ability to learn from data rather than being explicitly programmed. Instead of writing out rules, we train models on data—and they find the patterns for us.

If AI is the idea of simulating intelligence, ML is the method we commonly use to achieve it.

Examples of ML in action:

ML powers many of the tools we talk about in modern smart factories.

Neural networks are a specific Machine Learning technique that mimics how the human brain works. They’re comprised of layers of nodes (like neurons) that process information in stages, passing signals forward and adjusting as they learn.

They’re particularly good at:

In manufacturing, neural networks might be used to analyse vibration data from equipment to detect early signs of failure that a simpler model might miss.

Deep Learning is a subset of neural networks with many layers, allowing the system to learn complex abstractions. This is the type of AI behind things like:

For manufacturers, Deep Learning can unlock powerful capabilities in:

Tools like Proficy CSense make it easier than ever to apply these techniques at scale—without needing to write code or hire a data science team.

Why does it matter if we understand the difference between AI and ML?

Because it helps us ask the right questions:

It also helps you cut through the buzzwords. When a vendor says their system “uses AI,” you can dig deeper:

The Bottom Line

AI is the big vision. ML is how we get there. Neural networks and deep learning are powerful tools within that journey.

And now, more than ever, those tools are available to everyone.

The data you already collect through SCADA, Historian, or your MES? That’s the fuel. Tools like CSense and platforms like HighByte make it easier to connect that data, apply the right models, and surface the insights that drive action.

So, the next time you hear the term AI—you won’t just nod along. You’ll know which layer you’re in and where your operation could go next.

Written by: Martin Paczona – Head of Industrial Data Science

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