How to distinguish between AI, machine learning, and deep learning

Author:- Careers of Tomorrow
15/02/2019

Know how to tell the difference between AI, machine learning, and deep learning. To do so, first understand these complex systems and where the fields are headed - before they take over.

Artificial intelligence is characterized by numerous terms that you read almost everywhere; and most of the times used interchangeably. Read through to know their difference.

Artificial Intelligence is an algorithm delegated with taking care of input problems based on accessible information and operational parameters, with regards to the computational power accessible to the algorithm. Simply put, AI is the name for machine intelligence.

Similarly as Russian Matryoshka dolls are put inside the other, Deep Learning, ML and AI, the three segments is a subset of the other. Advances in these three fields are as of now changing numerous aspects of life, and albeit related, they are not the same.

Let’s start with the biggest doll "artificial intelligence".

 

1. Artificial Intelligence

As a part of computer science, AI is an area of research expecting to reproduce the different cognitive of human awareness, particularly the ability to take care of complex problems, in machines. In fact, that is one broad definition of AI, which is a wide domain and open to other logical and technological controls.

Artificial intelligence may allude to NPCs (non-player characters) in computer games, systems for image recognition, platforms for voice and speech, self-driven vehicles, predictive algorithms and other specialized computer programs.

Every one of these forms of AI has a common thing: they depend on pre-defined input; they need to be programmed before they can carry out a task. This brings us to the next level, machines that self-learn.

 

2. Machine Learning

Machine Learning, a subset of Artificial Intelligence centers around learning capabilities, or how machines can learn by themselves. Without having to hand-code instructions, ML system gains access to extensive datasets, apply their insight, learn from mistakes to finish a particular task.

For instance, IBM's Deep Blue (in 1997, it beat Garry Kasparov, the chess master) is not entirely a Machine Learning system since it couldn't cross-reference previous moves and matches.

The Google AlphaGo was able to beat Lee Sedol at the game of Go. This is a machine learning platform that reviewed hundreds of past plays to illuminate its strategies.

ML systems filter through information, learn patterns and anticipate results, that why Machine Learning tools are at the highest point of interests for data-driven organizations.

 

3. Deep Learning

As a subfield of Machine Learning, Deep Learning is a technology that dependent on deep neural systems. When you know the distinction among AI and terms that further characterize it, you are familiarizing yourself with the concepts.

A Deep Learning algorithm is made of layers of artificial nodes or "neurons", forming sort of a virtual computer where each layer executes calculation that fills in as an input to the following layer. That means that there will be huge gains in time and proficiency of the entire system.

 

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