AI vs. Machine Learning vs. Deep Learning

It's Time to Learn the Difference

Artificial intelligence (AI), machine learning, and deep learning have become fixtures of our lives. Today, we depend on these technologies to do our work and carry out everyday tasks.

Although the terms are sometimes used interchangeably, these technologies are not one and the same. Their relationships to one another are often compared to those of nesting dolls, in that machine learning is a subset of AI, and deep learning is a subset of machine learning. 

We see AI, machine learning, and deep learning in many different contexts, from voice assistance to speech recognition to driverless cars, so it’s extremely important and useful to know the differences between them.

AI vs. Machine Learning vs. Deep Learning 2

What Is AI?

Even before the “birth” of AI in the mid-20th century, writers, artists, philosophers, technologists, and others had been envisioning a world in which machines would behave and think like humans. We can see what have turned out to be prescient ideas in the masterpieces and inventions that have appeared throughout history.

AI is a branch of computer science, one that allows machines to mirror human intelligence and behavior. With the goal of solving complex problems — sometimes more complex than its human being counterparts can handle — it’s focused on processing data to behave in a similar manner to that of humans. 

AI is classified as weak or strong. Weak AI performs relatively simple individual tasks very well. However, given that these tasks aren’t very complex, it doesn’t rise to the level of successfully mimicking humans aside from completing the simple responsibilities at hand.

Strong AI, meanwhile, aims to more closely mimic human behavior, performing tasks and responsibilities that are far more complex than those that weak AI tackles. It is able to act independently, without the assistance or interference of human beings. This is particularly essential in high-stakes situations, as it happens with self-driving cars.

AI has already made an appearance in numerous industries, such as healthcare, finance, retail, gaming, entertainment, human resources, travel, education, and even creative disciplines like art and literature. In the future, we’re likely to see it pervade even more fields and niches.

What Is Machine Learning?

Machine learning, a subfield of AI, does what it sounds like it does — it allows machines to learn. Without human intervention, the technology is able to adapt, change, and grow, no explicit programming required. Neural networks, otherwise known as algorithms, work to solve problems and complete tasks.

Much like the human brain, every time the program repeats a task, it learns and is able to commit the process to “memory,” improving the process of completing the given task over time. This is because it’s better able to make predictions about how the data will behave. 

In fact, machine learning algorithms depend on you “feeding” them data. The more data with which they have to grapple, the more the process will improve. This is otherwise known as training the technology to reduce the instances of errors and become more and more sophisticated and intelligent, as well as make better-informed decisions about how to proceed next.

This is similar to how humans learn. As we take in more data and repeated processes over time, we learn and begin to recognize the material. Then, we’re better able to adapt to different situations, because we remember how we addressed problems and tasks in the past.

Take Netflix’s entertainment recommendations, for example. Using data like your viewing history, others with similar preferences, and how you’ve rated different television shows and movies, Netflix curates suggestions for shows and movies it believes you would enjoy. The more content you watch and rate, the better and more accurate the platform’s recommendations become, given that it has more data to work with.

Machine learning is also the basis of tools like image and speech recognition. For example, the more pictures of yourself you feed Facebook, the better able the social media app is to recognize your face.

What Is Deep Learning?

As we’ve discussed, deep learning is a subfield of machine learning — the smallest nesting doll, so to speak. Of the three terms, it’s the one used to tackle the most complex problems. Utilizing an artificial neural network, deep learning enables machines to assess data. It can do this independently of a human.

Like machine learning, deep learning requires an abundance of data to function. However, this subfield goes a step further, addressing even more complicated issues. 

To explain deep learning, it’s important to delve into artificial neural networks and how they work. Artificial neural networks involve sets of algorithms and have numerous layers. Data pervades the layers, facilitating a transfer of information. These neural networks can remember data inputs and patterns — they are self-learning, as long as they have large amounts of data at their disposal. 

Deep learning is often described as the form of AI that most closely resembles human behavior because it allows the machine to learn without previous knowledge of the circumstances.

Driverless cars are one obvious example of deep learning in practice. These vehicles are capable of recognizing the differences between objects and road obstructions, spot pedestrians, interpret signage, and so on — all thanks to deep learning.

If this all sounds incredibly complex to you, you’re not alone. Technology has advanced thanks to the hard work of skilled minds, extending far beyond what many of us could have possibly imagined. Where will it go in the future? Is superintelligence on the horizon? It’s unlikely that we’ll see the days when machines could replace humans entirely. Instead, we’re more likely to find more ways to use it to our advantage, improving our work and everyday lives.


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More about Machine and Deep Learning

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    What is AI?

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    What is Machine Learning?

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    What is Deep Learning?

AI is when a machine attempts to simulate human intelligence. Systems that use AI include natural language processing, speech recognition, and machine vision. One of the most prevalent examples of AI is the chatbot, which is an artificial intelligence program that simulates human conversation. Chatbots are widely deployed by businesses as the first line of contact with customers. A chatbot will interact with a customer to either try to resolve an issue or, if it’s incapable of doing so, pass the user on to an actual human. 

Chatbots are incredibly useful, efficient, and cost-effective solutions for businesses. Because chatbots can learn based on the input they receive, they are capable of making changes based on patterns. The more interaction they have, the more they learn.

That’s artificial intelligence at its core. In the end, the goal of AI is to make a machine behave in ways that would be called “intelligent” when compared to actual human behavior. In simplest terms, AI is programming machines to behave like humans.

But where does Machine Learning come into play?

Now that you have a fundamental idea of what Artificial Intelligence is, let’s take a look at Machine Learning. Machine Learning takes AI one step further. Artificial Intelligence requires human input for it to actually learn. The goal of Machine Learning is to make it possible for machines to learn things without having to program those things in the first place. 

Machine learning is a subset of AI and is one method of achieving Artificial Intelligence. The goal of Machine Learning is to create a piece of software, feed it data, and then allow it to use that data to learn and improve over time.

One very good example of Machine Learning is recommendation engines on Amazon. Every time you visit Amazon and search for (or purchase) a product, Machine Learning uses the data it receives from your searches and purchases to learn how to better recommend products for you. At first, the recommendations will seem off the mark. Over time, however, the system learns more and more about you and is capable of better recommending products you might actually want to purchase. 

That’s Machine Learning.

It’s also important to understand that all Machine Learning is a form of Artificial Intelligence, but not all Artificial Intelligence is a form of Machine Learning. Our chatbot example is not Machine Learning. However, you could design a chatbot with Machine Learning that would be capable of learning in such a way as to be better capable of answering questions. At that point, you’d have a Machine Learning-based chatbot. 

The key difference is that the Machine Learning chatbot is capable of actually learning from the input it receives. One of the most popular methods is Natural Language Processing (NLP), which refers to the interaction between computers and human language.

A true Machine Learning chatbot should be able to:

  • Offer an informative answer.
  • Maintain the context of the dialogue with the interacting human.
  • Be indistinguishable from the human.

The biggest hurdle is the final point. This is where the Turing Test comes into play, which is a  method of inquiry in Artificial Intelligence to determine whether or not a computer is capable of thinking like a human being. 

Eugene Goostman is the only chatbot that some have considered as having passed the Turing Test.

Languages used for Machine learning include Python and Java.

And now we get to Deep Learning.

Deep Learning is a subset of Machine Learning (which, in turn, is a subset of Artificial Intelligence). Where Machine Learning is accomplished by humans feeding information to a machine, Deep Learning accomplishes the same task through the use of a specific algorithm type called an Artificial Neural Network (ANN).

The biggest difference between Machine Learning and Deep Learning is that in Machine Learning the learning process is supervised. Let’s go back to our cat example. In Machine Learning a developer must be very specific about what things the program should look for to determine if an image contains a cat. Maybe pointy ears, fur, almond-shaped eyes, four legs, and a tail. 

With Deep Learning, the program builds this collection of details on its own, and eventually (once it’s gathered all of the necessary bits that can determine if a picture is, in fact, a cat), it can then pick out images of cats in photos.

With Machine Learning, the developer must tell the program how to determine if a cat is in a photo. With Deep Learning, the algorithms (over time) piece together everything it needs to determine if a cat is in a photo. One is supervised, one is not.

One of the more popular languages for Deep Learning is C++.

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