Understanding AI, Machine Learning, Deep Learning and the Differences Among Them

Understanding AI, Machine Learning, Deep Learning and the Differences Among Them

People’s perception of Artificial Intelligence until now has been determined by what they see on television. Jarvis and Ultron from Avengers, One from Bicentennial Man, OS Samantha from Her, thedystopian future from The Matrix, almost everything from Black Mirror, and so many more. While these movies got the general premise of AI, the writers didn’t hold back from portraying it as the inevitable doom of humanity. 

The way AI and Machine Learning have been talked about in the media has led to several misconceptions about these terms. Moreover, because these are firmly rooted in AI, Deep Learning is also misunderstood. 

As a result, many people have started using terms like Artificial Intelligence, Machine Learning, and Deep Learning interchangeably, without insight into what they mean. While these concepts are closely linked, there are significant differences in their applications, both practically and theoretically. 

Let’s look at what these terms mean individually, and what their key differences are. 

What is Artificial Intelligence?

Artificial Intelligence (AI) can be defined as any technology that uses human-like commands to deal with complex problems. AI projects are designed to perform tasks with a human approach and learn through them so that they can perform these challenges just the way humans do, or even better than humans. The goal is for the AI to learn through problems to do better in the future. 

AI is the application of a psychological principle called classical conditioning. It’s used to describe the changes that occur in one’s behavior due to repeated exposure to a situation. It’s also known as learning through association. In other words, our brains learn through association, and AI learns through algorithms. Research areas for AI are largely dedicated to human tasks where the machines haven’t been successful before. For example, communication, reasoning, emotions, etc. 

While data scientists use algorithms in AI projects, there is a very fundamental difference between a general computing algorithm and an AI algorithm. Traditional algorithms are designed to dictate a certain output for each input that the software receives. However, an AI algorithm is designed to make its own rules depending upon the command it receives. This technique makes it possible for computers to carry out human tasks such as customer service, driving cars, or reasoning. 

If these algorithms are written with specialized tasks in mind, the AI can perform them to completion with absolute precision. Predominantly, this advancement in Artificial Intelligence is due to Machine learning. However, there are some noteworthy differences between the two. 

What is Machine Learning?

Machine learning is the mechanical aspect of artificial intelligence. Better defined as the application of AI, it’s the backbone of artificial intelligence and the vision behind it. It’s the foundation that supports the concept of AI and turns it into a reality. 

In simple words, AI functions through machine-learning algorithms. As we’ve seen above, machine-learning algorithms, when compared with general algorithms, have a vast difference that enables AI to mimic human behavior. Such algorithms are designed to learn from new inputs, making it possible for them to take action without human interference. 

Therefore, AI is the product and machine learning is the means that data scientists use to get there. This is why AI and machine learning are two terms that are often used interchangeably. 

Some of the most popular machine learning free development tools used today are:

If you’re looking for ML as Service, both Amazon and Google provide top quality SaaS solutions:

  • AWS (Amazon Web Services) – AWS is a cloud computing platform that provides computing power, storage, content presentation, to help businesses grow. It allows users to add intelligence to their applications. For example, Amazon SageMaker is a means for developers and data scientists to make various machine learning models. It acts as a common space for integrating different tools like Amazon Augmented AI, which adds a human reflection to model forecasts. Amazon Comprehend is a natural language processing (NLP) program that applies machine learning to understand the properties of text in unstructured data. For instance, how positive or negative the text is, or company names in reports. 
  • Firebase MLKit – Firebase MLKit incorporates machine learning programs such as face recognition, language translators, etc. It’s a software development kit (SDK) for mobile developers that can be used both on the cloud and the device. It can be easily integrated with both iOS and Android apps. 

What is Deep Learning?

Deep learning is a subset of AI that uses data processing programs to handle large amounts of data. Many people think of deep learning as a subordinate of machine learning, but they’re two very different fields. The difference between machine learning and deep learning is that there are no pre-existing algorithms in deep learning. 

Deep learning is based on the connection of neurons in the human brain known as neural networks. To understand deep learning, you must know what neural networks are. In this case, they are a form of machine learning that tries to create the same mechanism as the processes that the human brain uses to perform various tasks such as decision making. 

It’s a course of algorithms that seeks to detect hidden layers in a group of data just like the human brain. In neural networks, there are artificial neurons that are called nodes. When we look at an artificial neural network, we see that the input and output are connected through a series of layers. A deep learning algorithm can pass through more layers than machine learning. Because of this program, deep learning possesses the capacity to imitate human behavior in its entirety. 

Deep learning takes artificial intelligence a step further and would help create an AI that can read through underlying data and perform original output the way human brains have been doing for a long time. 


In conclusion, when it comes to machine learning, it is important to understand what each part of it implies. This is vital to understanding how artificial intelligence works and what the future of AI holds. 

AI is a program that’s trained to deal with problems the way humans do. This technology is made possible through machine learning algorithms that help read layers of input data to perform tasks. Deep-learning increases the potential of reading concealed data and processing it to program AI further to behave like humans. Hence, AI is the end result that data scientists hope to reach with the help of machine learning and deep learning. 

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