The Difference Between AI, Machine Learning and Deep Learning

Artificial Intelligence has improved considerably in the past few years, not only in terms of inventions and researchers but also in terms of famousness. Whether you are a Tech Geek or not, you know AI very well in the current era, the thing that somehow confuses things is the use of AI, Machine Learning, and Deep Learning interchangeably. This article focuses on explaining these three terms and also the difference between them in layman terms.

What is Artificial Intelligence (AI)?

As fully evident from the name, this term means “Intelligence that is Artificial.” Artificial means two things here:
  • It is found in and developed for machines
  • It is developed and executed using computer algorithms
To be clear, AI is merely the ability of machines to make logical decisions and do intellectual tasks by themselves, mostly compared to humans. There are three levels of AI:
  • Narrow AI is the stage at which a machine is more efficient than humans at a specific task but is not good at others.
  • General AI marks the level when a machine is as intelligent as a human being; automates many analytical tasks.
  • Strong AI is when a machine outperforms humans in a number of tasks; the ones we can do or cannot do.
Just to note that we are at the Narrow AI level today!

What is Machine Learning (ML)?

If you ponder a little, the term simply marks “the ability of a machine to learn.” It is the field in which instead of developing an algorithm and complex code for a computer to do a specific task, researchers develop an algorithm to give the machine the potential to teach itself. As we learn from examples, machines also learn or train themselves from past examples or data (Big Data or Data Science). For which, a complete data set is given to a machine and it goes over it to know how to perform that function.

For example, a machine can be thought of as a child who watches his trainer play piano and thus learns which key makes what sound and then he can play piano and even compose his own tunes with some practice. Similarly, a machine studies a given data set that provides all the cases of a specific job and becomes good at it.

What is Deep Learning (DL)?

The most efficient way to learn is to recognize a pattern in the way of doing a task or in the answers given to similar questions. Deep Learning is “automated pattern recognition.” It analyzes different chunks of data systematically and tries to simplify a problem for easy learning.

Deep Learning makes use of “artificial neural networks” for this purpose. Data is entered from one side and it is passed through different layers. It is called a network because of these layers. The layers can be thought as different levels through which data is passed to break it down; a small part of the pattern is recognized at every layer and a small part of the problem is solved at every layer.

The Crux:

In layman terms,
Deep Learning is an efficient method to achieve Machine Learning and Machine Learning is a way to achieve Artificial Intelligence.
So, they are not three different things but just smaller versions of each other. Do you still have any questions regarding the three terms or their difference? Feel free to ask in the comments, we’ll be more than happy to clarify!

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