The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning

Technology continues to evolve. Who would have dreamt of smartphones, Alexa, electric cars, and all the modern technology we see today, back in the 90s. It’s incredible to see devices around us with intelligence sometimes surpassing the human minds.

You may ask what made this possible? The answer is Artificial Intelligence. You must have heard about Machine Learning, Deep Learning, and Artificial Intelligence before, probably thousands of times.

If a machine is able to make a decision on its own, this intelligence is accredited to these three. But very few people are clear about these, what’s exactly the difference between Artificial Intelligence, Machine Learning, and Deep Learning?

In this article, I’m going to discuss all three in detail along with their examples, so anyone having ambiguities regarding their use in the real world would have their doubts cleared.

What’s Artificial Intelligence?

You may have come across hundreds of definitions of AI, more or less all of them mean the same thing. 

Here’s John McCarthy’s definition that he wrote in 2004’s paper,” It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable."

Let’s make it simple. AI is a field that combines both computer science and datasets, ultimately creating a powerful problem-solving system. And yes, machine learning and deep learning are also part of this system.

Together, these technologies create expert systems that are capable of making intelligent decisions and learn over time based on their past experiences and data fed to them.

Example of Artificial Intelligence

There are hundreds of examples of AI around us. But let’s just take the most simple ones, so you can get your doubts and confusions cleared.

Websites today use chatbots to improve customer experience. But what makes them intelligent, how come they are able to talk and answer your queries like real human beings? The answer is artificial intelligence.

Basically, they are programmed algorithms. Artificial intelligence app development is quite popular these days. AI experts program them with the frequently asked questions in mind. Some of them are so intelligent that they can even take and track orders, and direct calls.

Further, they can impersonate the real customer support representative, their style of conversation to tone, so the customer doesn’t feel like interacting with a robot.

With rapidly advancing technology, it’s possible to rectify common mistakes. A bad rating will signify the bot to identify the problem and prevent the same mistake in the future to ensure maximum customer satisfaction.

What is Machine Learning?

As mentioned before, machine learning comes under the umbrella term of artificial intelligence. Machine learning empowers a machine to automatically learn and improve the experience, without even being programmed on a regular basis to deal with new problems and complex scenarios.

Basically, it focuses on the development of computer programs that can access data and use them in the future, for making intelligent decisions. The more a machine is exposed to new environments and situations, the quicker it learns.

The learning never stops; a machine keeps learning basic on its observations and given data. The primary aim of machine learning is to make computers intelligent, so humans don’t have to program them for small tasks.

Companies are spending profusely on machine learning app development to create apps that are intelligent and can get along on their own.

So, as far as the difference is concerned, AI is a broad term and ML is a part of AI.

Example of Machine Learning

Everything that’s part of AI is automatically related to ML. However, to make the two easier to distinguish or comprehend for you, let me walk you through an example.

Image recognition and speech recognition both are perfect examples of machine learning. 

Today, a device can identify an object as a digital image. You may ask how? Thanks to machine learning, a device can pick up the intensity of pixels in an image and based on it recognize an image.

It’s because of machine learning that an X-ray can be labeled as cancerous or normal. Likewise, crime investigation and law enforcement departments can recognize handwriting by segmenting a letter into different pages.

The same goes for speech recognition. A machine can translate speech into text -- haven’t you noticed voice typing has become quite popular these days?

The software we use for our day-to-day tasks for translating speech into texts are examples of machine learning. 

Appliance control is another example of machine learning. People in developed countries use speech recognition software like Alexa to send instructions to their air conditioner, tower fan, and all other appliances, even if they are away from home.

What is Deep Learning?

Machine learning is a part of artificial intelligence, and deep learning is a subset of machine learning. It’s a system of neural networks that stimulates the behavior of the human brain. 

Have you ever noticed how a human brain learns from its past experiences? When it learns touching a hot cup can leave you with a burn, it never lets you repeat the same mistake. 

The neural network inside our mind keeps learning from large amounts of data, improves our analytics abilities, and makes us intelligent.

The concept of deep learning in AI and ML is the same. It improves automation, makes a machine energy efficient when it comes to performing analytics and physical tasks, and makes it independent of human intervention to keep the operations streamlined.

Example of Deep Learning

Every deep learning example is actually also an example of artificial intelligence and machine learning.

Driverless vehicles are the biggest example of deep learning. Cars have to constantly react according to the change in environment to prevent accidents and damage to the car.

With the help of patterns formed, a car learns it’s important to stop the car when someone is crossing the road, or come right in front of it. Sensors and deep learning algorithms help a car to accomplish this task successfully.

The more data the car’s algorithm gets, the quicker it learns and drives as safely as humans do.