Machine Learning vs. AI vs. Deep Learning

It can be challenging to keep track of all the terms you see in the tech community. It doesn’t help that a lot of them are related or may overlap with others. If you’re confused about the difference between machine learning vs. AI vs. deep learning, you’re not alone.

You’ve probably heard people use all of these phrases interchangeably, but that’s not correct. Machine learning, AI and deep learning are all connected, but they’re not the same thing. Given how close they are, though, it can be challenging to distinguish between them.

On the one hand, framing these distinctions as a conflict between machine learning vs. AI vs. deep learning can be misleading because they’re closely connected. On the other, they are distinct from one another, so you can’t use them interchangeably. So what’s the difference between them?

The short version is that deep learning is a type of machine learning, which is a subset of AI. To better understand the distinctions between them, it helps to know more about each one. Here’s a closer look.

Artificial Intelligence (AI)

Let’s start with the broadest of these categories: artificial intelligence, also called AI. You’re probably more familiar with this one than the others, but may still be fuzzy about it. There are multiple ways to define AI, but most people agree that it refers to machines replicating human intelligence.

You might’ve seen the terms “strong AI” and “weak AI” before. Strong AI refers to machines with actual intelligence, like what you see in sci-fi movies. Weak AI, which is what we have now, is about technology that only seems like it has human intelligence.

As you might’ve noticed, these definitions are rather vague, and that’s because AI is a broad category. A lot of processes mimic human intelligence, so a lot of things can count as AI. For example, the autocorrect feature on your phone is a type of AI since it can recognize and interpret speech like a human.

Autocorrect is a specific form of AI called natural language processing (NLP). Other types of AI include machine vision, where a computer can recognize objects, and expert systems, where computers make decisions based on a logical framework. All of these applications are different from one another, but they’re all under the AI umbrella.

Some machines use several of these different AI branches together. Digital assistants like Alexa, for instance, use NLP to interpret speech, but they do more than that, too. There are also some AI categories that have subsets of their own.

These subcategories are where the distinctions between machine learning vs. AI vs. deep learning lie. Machine vision and deep learning are both specific types of AI. Now let’s look at the specifics of machine learning vs. AI.

Machine Learning (ML)

Machine learning is a specific branch of AI and an especially widespread one at that. A lot of the AI applications you’ll hear about use machine learning, so you can see how people may confuse the two. All machine learning processes are AI, but AI goes far beyond just machine learning.

Think of it like squares and rectangles. All squares are rectangles, but not all rectangles are squares. The same is true of the distinction between AI vs. ML.

This type of AI focuses on finding patterns in data through algorithms and statistics. It takes sets of data and looks for connections between them to “learn” something, hence its name. You see this process in action all the time in things like targeted ads and YouTube recommendations.

You’ll often see ML alongside other AI subsets, like in our Alexa example. Alexa uses NLP to understand what you say to it, and as you talk to it, it uses ML to learn your user preferences. It’ll find patterns in the things you say and ask for, then teach itself to accommodate those in its answers.

According to Microsoft, a leader in machine learning systems, researchers follow four basic steps to create ML. First, they feed data into an algorithm, then use that data to train an ML model. They then test this model, giving it new information to see how it learns from it and making any adjustments. Finally, they let it start repeating this process on its own, and the machine starts teaching itself like the researchers first taught it.

Machine learning is all about finding and applying patterns, which is similar to how humans think sometimes. Since it resembles human thought, it counts as AI. So that’s the rundown of machine learning vs. AI, but where does deep learning fit into this?

Deep Learning (DL)

Like with AI vs. ML, deep learning isn’t so much a separate technology from machine learning, but a subset of it. It also deals with finding patterns in data sets but goes a step further. Using layers of algorithms called deep neural networks, it works similarly to how the human brain does.

You can think of deep learning as the next step in machine learning techniques. It also searches for patterns but is much better at doing so than other, older types of machine learning. Deep neural networks don’t always process data linearly, so they can make sense of massive pools of unstructured data.

Deep neural networks typically consist of multiple machine learning algorithms on top of one another. In that way, you can think of deep learning as several layers of machine learning. Since it has so many layers, it can make more informative analyses or work with less organized data.

Unlike other forms of machine learning, deep learning can determine how to organize data on its own. It requires far less human input than other machine learning applications. As a result, these systems can learn without human intervention.

If DL is a more advanced version of ML, then why doesn’t everyone use DL instead? While deep learning can do more, it also requires a lot of data and time to train, and it uses advanced expensive machinery. Not every application is worth the extra effort and expense.

Understanding the Different Forms of AI

Looking at AI vs. ML vs. deep learning, it’s easy to see how people can get them confused. There’s a lot of crossover between the three terms, so if you don’t understand them, you might think they’re all the same. Knowing the differences can help you better understand people when they talk about one or more of these subjects.

When you understand these technologies, you’ll start to notice them everywhere. While deep learning isn’t quite widespread yet, you can find examples of ML and AI in your everyday life, from your phone to your social media feed.

AI, and its subsets of machine learning and deep learning, are shaping the future. Learning more about these technologies can help you process how the world is shifting.