Quick overview: what is AI (Artificial Intelligence), ML (Machine Learning) and DL (Deep Learning)?

TL;DR :
Deep Learning (DL) is a subset of Machine Learning (ML)
Machine Learning (ML) is a subset of Artificial intelligence (AI)

What is artificial intelligence?

Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence.

As the name suggests, artificial intelligence can be loosely interpreted to mean incorporating human intelligence into machines.

Whenever a machine completes tasks based on a set of stipulated rules that solve problems (algorithms), such an “intelligent” behavior is what is called artificial intelligence.

For example, such machines can move and manipulate objects, recognize whether someone has raised the hands, or solve other problems.

AI-powered machines are usually classified into two groups — general and narrow. The general artificial intelligence AI machines can intelligently solve problems, like the ones mentioned above.

The narrow intelligence AI machines can perform specific tasks very well, sometimes better than humans — though they are limited in scope.

The technology used for classifying images on Pinterest is an example of narrow AI.

What is machine learning?

As the name suggests, machine learning can be loosely interpreted to mean empowering computer systems with the ability to “learn”.

The intention of ML is to enable machines to learn by themselves using the provided data and make accurate predictions.

Machine learning (ML) is a subset of artificial intelligence; it’s a technique for realizing AI and a method of training algorithms such that they can learn how to make decisions.

Training in machine learning entails giving a lot of data to the algorithm and allowing it to learn more about the processed information.

What is deep learning?

As earlier mentioned, deep learning (DL) is a subset of machine learning; in fact, it’s simply a technique for realizing machine learning. In other words, DL is the next evolution of machine learning.

DL algorithms are roughly inspired by the information processing patterns found in the human brain.

Just like we use our brains to identify patterns and classify various types of information, deep learning algorithms can be taught to accomplish the same tasks for machines.

The brain usually tries to decipher the information it receives. It achieves this through labeling and assigning the items into various categories.

Whenever we receive a piece of new information, the brain tries to compare it to a known item before making sense of it — which is the same concept deep learning algorithms employ.

Comparing deep learning vs machine learning can assist you to understand their subtle differences:

• While DL can automatically discover the features to be used for classification, ML requires these features to be provided manually.
• Furthermore, in contrast to ML, DL needs high-end machines and considerably big amounts of training data to deliver accurate results.