AI vs. Machine Learning vs. Deep Learning: Clear the Cobwebs

If you’re confused by artificial intelligence, machine learning, and deep learning, you’re not alone. Often, these terms get used interchangeably, but they’re not the same thing — though they belong in the same family, they are all different in their own way.

AI vs. machine learning vs. deep learning — we’ll go over the definition and uses of each term, including how they work together, below:

• What is AI?
• What is machine learning?
• What is deep learning?
• AI vs. machine learning vs. deep learning: What’s the difference?
• How AI, machine learning, and deep learning fit together
• Limitations with AI, machine learning, and deep learning

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What is AI?

AI refers to the broader concept of machines mimicking human intelligence for tasks such as learning, problem-solving, and decision-making.

But how does AI work exactly? For a machine or computer to be artificially intelligent, you have to train it with algorithms, or a list of rules and regulations. An algorithm is fundamental to training a computer or machine to be AI-equipped. 

In this list of rules, you train your machine what to do if “something” happens. Providing “if/then” style information to a machine teaches it how to react if a particular situation arises, and the longer it’s exposed to those “if/then” situations, the better it’ll become at solving them.

Example of AI

A simple example of artificial intelligence at work would be bot marketing via a chatbot on a website or Siri on your smartphone. These applications prove that machines can be trained to act like humans to accomplish a specific task.

Keep in mind that artificial intelligence is the biggest, overarching group when talking about machine learning vs. AI vs. deep learning. That means everything classified as machine learning or deep learning is a part of artificial intelligence.

Our tool, RevenueCloudFX is another example of an AI content tool. This marketing automation suite utilizes artificial intelligence to bring amazing results to businesses marketing and advertising online. It can provide clients’ businesses with data-backed recommendations when it comes to their marketing strategies.

Bonus Read: Artificial Intelligence vs. Human Intelligence

What is machine learning?

Machine learning, a subset of AI, refers to the way that a computer system can learn based on the provided information.

Machine learning enables systems to autonomously learn and enhance their performance based on experience without any explicit programming. It is centered around creating computer programs that can process and utilize data to self-learn.

The goal of machine learning is to train a machine to know what to do in a specific situation based on the data provided. So, to train a machine with machine learning, you’ll have to provide it with large amounts of data. A computer picks up on patterns and trends in the data you give, which allows it to learn and understand the most likely outcomes.

Types of machine learning

There are three main types of machine learning, which describe the algorithms used to train each machine learning tool. Those three types are:

• Supervised learning: In this situation, models are trained with labeled data, which means they already know the target variable. If you show a model 1,00 pictures of trees and note that they are trees, the tool will be able to identify a tree.
• Unsupervised learning: Conversely, in unsupervised learning, models identify patterns in unlabeled data. The model learns to find the patterns within the data, which makes it easier to detect patterns moving forward.
• Reinforcement learning: With reinforcement learning, models learn through rewards and penalties.​ This learning process requires much trial and error for the model to learn patterns in an unstable environment.

Example of machine learning

Say there’s a conveyor belt at a factory that separates glazed doughnuts from doughnut holes. When operators trained the belt with machine learning, they probably fed it data that looks something like this:

• Left belt: 2-5 oz.
• Right belt: 5-10 oz.
• Left belt: 1 unit
• Left belt: 5+ units

The doughnuts would then be sorted based on this data.

However, if a doughnut entered the belt that was 12 oz, the machine wouldn’t know what to do since that wasn’t a part of its training. That’s where deep learning comes in.

What is deep learning?

Deep learning is a specific type of machine learning that uses artificial neural networks to process vast amounts of data and make complex decisions Deep learning enables tools to accomplish tasks such as classification, regression, and representation learning.

Deep learning is a “deeper” subset of teaching machines with the provided information. While you must feed a machine data for machine learning to work, deep learning can make conclusions on its own.

Types of deep learning architectures

To train deep learning models, there are two different architectures that programmers use:

• Convolutional neural networks (CNNs): Used in image and video recognition. CNNs work best with spatial data for things like facial recognition and video classification.
• Recurrent neural networks (RNNs): Used in natural language processing (NLP) and time series analysis. RNNs work best with sequential data for things like stock prices, temperatures, and sentences.

Example of deep learning

Let’s say you have a cat feeder that you have trained via machine learning to dispense food when you speak any phrase with the word “feed.”

But what if you say “The cats are hungry” instead?

With machine learning, the food dispenser wouldn’t react, but if a user trained the cat food dispenser with deep learning, it would be able to compute the meaning of your sentence to work the device.

AI vs. machine learning vs. deep learning: What’s the difference?

Now that we know about AI, machine learning, and deep learning, we can start comparing the three tools. This table breaks down the differences between them.

How AI, machine learning, and deep learning fit together

If every term has a different definition, how do they possibly fit together? The best way to connect and compare AI vs. machine learning vs. deep learning is to think of a target.

The outermost ring of the target illustrates artificial intelligence. AI is the overarching term that refers to the way that machines can be as smart as humans — and sometimes even smarter.

Machine learning, then, is the middle ring of the target. It’s a specific kind of artificial intelligence, and it refers to the way that you train computers (machines) to act like humans.

The innermost circle is deep learning, which is a further subset of machine learning. It can do even more than machine learning, and can essentially make decisions without much previous training. It’s best to think of AI vs. machine learning vs. deep learning as different subsets that are a part of the same family — each with its own importance.

Limitations with AI, machine learning, and deep learning

While AI is transforming the way we create content, market our businesses, and even diagnose illness, there are limitations to any model.

AI, for one, lacks understanding and common sense. There are plenty of ChatGPT fails that showcase the lack of understanding and limitations of AI generators and chat tools.

Machine learning tools are also limited to the data they are trained on. Unlike deep learning, they can’t expand their data points and reason.

And, even though deep learning is a powerful tool, it comes at a high cost — monetarily and environmentally. It takes massive amounts of data, time, money, and resources to invest in deep learning models, which many companies don’t have.

Activate AI in your marketing campaigns

If you’re still curious about the inner workings of machine learning vs. deep learning vs. AI, we understand! These terms can be challenging to differentiate, but we’re here to help.

WebFX knows AI so much that we’ve developed proprietary marketing software, RevenueCloudFX, which we use to supercharge our clients’ campaigns and their results. So far, we’ve helped our clients’ businesses earn more than $10 billion in revenue — and that’s only in the past five years.

To learn more about RevenueCloudFX and how it can use artificial intelligence to improve your marketing strategy, be sure to contact us online or give us a call at 888-601-5359!