De-Mystifying Machine Learning

A non-technical introduction for beginners

The terms “artificial intelligence”, “machine learning”, and “neural networks” are loosely thrown around nowadays. With all of the hype that is built around it in the current news, machine learning is often praised as a silver bullet — a magical technique that can solve ANY problem, no matter how complicated.

Well, as you’d probably expect, that’s not completely true. What is true is that modern machine learning (ML) techniques have been able to perform tasks that were previously seemingly impossible. Not only that, compared to solutions we did have for certain tasks, but modern ML techniques have also proven to be better in almost every way: they are faster to develop, more robust, and often run faster. However, these techniques come with their own limitations that make them better suited to certain applications. So while the field is constantly evolving with hundreds of researchers all around the world expanding the state-of-the-art, it’s important to understand the fundamentals of how it works so that we can apply it to solve the problems at which it excels.

Define the terms

Let’s first define the 3 most commonly used “buzz word” terms in this industry:

Artificial Intelligence — The theory and development of computer systems able to perform tasks that normally require human intelligence [1]