What's the Difference Between A.I. and Machine Learning?

Artificial Intelligence (AI) and Machine Learning (ML) are two terms that are often used interchangeably. With the rise of programs like ChatGPT, Jasper A.I., Canva Magic Write, Notion A.I., and more, this is as important reminder that A.I. and M.L. are not the same thing. While both of them are related to the field of computer science, they have different meanings and applications.

A.I.

Artificial Intelligence is a broad field that encompasses various technologies and techniques aimed at creating intelligent machines that can simulate human cognitive processes. In essence, A.I. is the general concept of creating machines that can perform tasks that typically require human intelligence.

Machine Learning

Machine Learning is a subset of AI that focuses on designing algorithms that can learn from and make predictions or decisions based on data. Machine Learning algorithms are designed to analyze data, identify patterns, and make predictions or decisions without being explicitly programmed to do so. In other words, machine learning is a method of teaching machines how to learn and improve based on experience, without being explicitly programmed.

Here’s an Example:

To better understand the difference between AI and Machine Learning, consider the following example:

Suppose you wanted to build a machine that could recognize images of cats. One way to do this would be to manually program the machine with a set of rules that define what a cat looks like. However, this approach would be time-consuming and would require an enormous amount of data to cover all possible cat images. Alternatively, you could use Machine Learning to train a machine to recognize cats by feeding it thousands of cat images until it learns to recognize the common features that make up a cat. This approach is more efficient and requires less manual intervention.