Artificial intelligence vs Machine Learning vs Deep Learning

Artificial intelligence vs Machine Learning vs Deep Learning

There is lots of confusion around these three terms: Artificial Intelligence, Machine Learning, and Deep Learning.  Most of the people believe that these terms reflect the same meaning and many times use these words interchangeably. In this article, we will try to understand the difference between these terms.

Machine Learning

Machine Learning, a subset of Artificial intelligence, is the study of algorithms and statistics models that the computer uses to perform specific tasks without any supervision. Machine learning algorithms build a mathematical model based on the training data.

For example, let’s say somebody asks you to predict the age of the child given height only. How did you solve this problem? Intuitively, you will tell the height of the child.

Now, if we put this intuition in the mathematical equation (it comes out as regression line equation)

                                                                         age = alpha + Beta * Height

(alpha and Beta values will be derived using historical data)

In the same way, the machine learning model will be trained. Based on the training data, alpha and Beta values will be calculated. So whenever next time you need to find the age, you just input the height.

Deep Learning

Now moving to deep learning, real-world problems are not so easy like our example. Sometimes it’s a complex relationship and requires complex models. It is the subset of neural networks and machine learning. Applications of deep learning are natural language processing, speech & audio recognition, and computer vision, etc.

Artificial Intelligence

As per Britannica, Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience.

Continuing with age prediction example, using a machine learning model you know the age of the child so how do you build a solution using this knowledge. Consider the business scenario that you are the owner of an amusement park and there are certain rides that allowed restriction on age as per the government law. But the problem is that it is difficult to know the age and possibility is there that information revealed may not be correct. So you trained the machine learning model which predicts the age based on the height. Then created automated solutions to allow children above a certain height. Although this is a naïve AI solution.

Hopefully, this article helped you to differentiate between AI, ML and Deep learning.

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