What is Artificial Intelligence (AI)?
Artificial Intelligence has nearly as many definitions as people you ask. Here are two intriguing definitions I have heard.
This is actually a pretty good definition, but it is always changing so it is hard to evaluate and compare. Thus, the following definition works better.
“Intelligence Demonstrated by Machines”Wikipedia
Now let’s dive into the word intelligence.
What is Intelligence?
According to Wikipedia, Intelligence is the ability to acquire and apply knowledge and skills. There are 2 keywords in that defintion: acquire and apply. For many years, computers have been very good at applying knowledge and skills. For example, computers can look up information way faster than a human. They can perform calculations much faster. When computers know what to do, they are very efficient.
Unfortunately, humans cannot tell machines what to do for every possible scenario. That is why true AI needs to also be able to acquire knowledge and skills. True AI needs to be able to learn.
When that learning involves extracting meaning from previous data, it is called machine learning.
What is Machine Learning (ML)?
A formal definition
“Machine learning is the science (and art) of programming computers so they can learn from data.”Aurelien Geron in Hands-on Machine Learning with Scikit-Learn and Tensorflow
The key part of this definition is the presence of data. Machine learning needs data to work. The data serves as past experience, so the algorithm can learn from that past experience.
What is the difference?
So there you have it. Machine Learning is a subset of Artificial Intelligence which needs prior data in order to learn. Below is a diagram to help visualize the concept.