Difference Between Artificial Intelligence, Machine Learning and Deep Learning

Artificial intelligence is a broad concept that encompasses everything from GOFAI to futuristic technology. 
Machine learning (ML) and deep learning (DL) are one way to achieve Artificial Intelligence (AI).
Most people think that all artificial intelligence, machine learning, and deep learning are the same. 
Whenever they hear the term Artificial Intelligence (AI), they directly associate that word with machine learning or deep learning.
Okay, these things are related to each other but they are not the same.

Here is a simple but significant explanation of artificial intelligence, machine learning, and deep learning.

A simple explanation of artificial intelligence, machine learning, and deep learning

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

Artificial Intelligence as a comprehensive concept 

The term "artificial intelligence (AI)" is very familiar to us, after all the hype and chaos that we have encountered in the past.

But you may have recently heard other terms like "machine learning" and "deep learning". Sometimes used instead of the term "artificial intelligence".

Artificial Intelligence is a comprehensive concept in which everything is included from "Good Old-Fashioned Artificial Intelligence (GOFAI) to deep learning like futuristic technology.

Some artificial intelligence systems can perform some specific and complex tasks very well, sometimes more excellently and more effectively than humans - though these techniques are limited in scope.

In this article, we will give you a quick explanation of what "artificial intelligence", "machine learning" and "deep learning" are and how they differ.

What is the Difference between Artificial Intelligence, Machine Learning, and Deep Learning?

The terms "Artificial Intelligence (AI), machine learning (ML), and deep learning (DL)" overlap with each other. That is why they can easily create some confusion but do not worry.

I will explain all these terms one by one with appropriate examples. So let's start!

Artificial Intelligence (AI)

Artificial intelligence as an educational or academic discipline was established in 1956 by John McCarthy.

At the time, the goal was to make such computers that can perform tasks like a specific human.

Thus, Artificial intelligence was defined as 

"Artificial intelligence involves machines that can perform tasks and duties easily and excellently that are characteristic of human intelligence".

While this is somewhat common, it includes tasks such as planning; identifying objects and sounds, understanding language, learning and problem-solving.

Artificial intelligence is used to control a robot or digital device using a computer.

It relies on imitating and mimicking the kinetic and mental processes of advanced organisms such as humans.

Since the development of computer systems in the 1940s, artificial intelligence has been evolving and entering into spheres of life more widely and effectively to perform human operations that require complex analytical and reasoning capabilities, such as: simulating chess well and proving mathematical theories.

Artificial intelligence can be placed in two categories; general and narrow.

The general category involves all the characteristics of human intelligence including the above capabilities.

The narrow category includes some aspects of human intelligence, which can do these tasks well, but lack other areas.

A machine that can only recognize images - and nothing else - may be an example of a narrow category of artificial intelligence.

Machine Learning(ML)

Machine learning is a branch of artificial intelligence. Basically, machine learning is just a way to achieve artificial intelligence and relies on the analysis of a large amount of data in record time.

This can then be linked to decision-making and future prediction processes, where the computer analyzes an enormous amount of data that a human cannot normally analyze and study easily.

In 1959, Arthur Samuel formulated the phrase shortly after the emergence of artificial intelligence and described machine learning as “the ability to learn without being explicitly programmed”.

Artificial intelligence can be obtained without the use of automated or machine learning, but this requires building millions of code lines with complex rules.

So instead of making programs that contain specific information to accomplish a particular task, machine learning is just a training method of an algorithm.

Training involves feeding the algorithm with large amounts of data and allowing it to adjust and improve itself.

Apart from the technological aspects of machine learning derived from information systems, the applications of these technologies are very enormous and useful to the maximum degree in various fields, and contribute significantly to decision-making processes and provide effort and time with the mechanism of accuracy.

Machine learning can be illustrated by such an example; it is used to make radical improvements to computer vision (the machine's ability to recognize an object in an image or video).

Deep Learning (DL)

Deep learning is a kind of machine learning and training to build an educated and intelligent model from a large amount of data.

This type of algorithm - DL- is built to learn the characteristics of Feature Learning without having to specify those characteristics in advance.

In addition, it is one of the best algorithms that enable the machine to learn different levels of data properties (e.g. images).

Deep learning includes other methods such as inductive logic programming, decision tree learning, reinforcement learning, clustering, and Bayesian networks, and others.

Deep learning is inspired by the structure and functions of the brain; the connection between many neurons. 

Artificial Neural Networks (ANNs) are algorithms that simulate the biological structure of the brain.

In Artificial Neural Networks, there are "neuronal cells" that have separate layers and connections to other layers of neuronal cells.

Each layer is responsible for the learning property, such as curves/edges in image recognition.

These layers are the ones that give deep learning this name, the "depth" is created by the use of multiple layers instead of a single layer.

Deep learning is distinguished in the creation of new characteristics that can be learned at different levels.

This will lead researchers in the future to focus on this very important aspect.

Features are the first factor in the success of any intelligent machine learning algorithm.

Your ability to extract and/or correctly select properties and to represent and prepare data for learning is the dividing point between the success and failure of the algorithm.

Related Post: How Artificial Intelligence and Internet of Things are Inextricably Intertwined?

Summary

⇒Artificial intelligence (AI) is defined as "it is human intelligence displayed and exhibited by machines that can perform tasks and duties easily and excellently that are characteristic of human intelligence.

⇒Machine learning (ML) is a branch of artificial intelligence that approaches to achieve Artificial Intelligence and relies on the analysis of a large amount of data in record time.

⇒Deep learning (DL) is a kind of machine learning which builds an educated and intelligent model from a large amount of data to implement Machine Learning.