What the heck is machine learning, and why is it everywhere these days? Posted on : Nov 19 - 2017

Unless you’ve been living under a rock, ignoring every big tech advance in the past decade, you’ve probably heard of machine learning. Whether it’s better fraud detection and prevention, the handy online recommendations made by Netflix and Amazon, revolutionary facial recognition technology, or futuristic self-driving cars, machine learning is powering the current artificial intelligence revolution. But what is it exactly? Here’s a handy beginner’s guide.

Machine learning is an approach to artificial intelligence that’s focused on making machines which can learn without being explicitly programmed. Learning is a profoundly important part of what makes us human. If we’re going to build AI that can carry out tasks with human-like intelligence, we therefore need to make machines that can learn for themselves, based on their past experiences.

This is different to the classical symbolic approach to AI, in which programmers create step-by-step rules for machines to follow, rather than allowing them to discover insights for themselves. While machine learning still involves this classical style of programming, it combines those basic rules with knowledge that computers are able to gather on their own to grow smarter.

Oh, and there’s a whole lot of statistics in there as well. Today, machine learning’s massive success has led to it becoming the most dominant subset of AI that is practiced around the world.

Absolutely. Machine learning can achieve some pretty impressive feats in AI (think self-driving cars or teaching robots to autonomously interact with the world around them), but it’s also responsible for simpler, but still incredibly useful applications.

One good illustration of machine learning in action is the so-called “spam” filter that your email system most likely uses to distinguish between useful emails and unsolicited junk mail. To do this, such filters will include rules entered by the programmer, to which it can add numbers that — when added up — will give a good indication of whether or not the software thinks the email is good to show you.

The problem is that rules are subjective. A rule that filters out emails with a low ratio of image to text isn’t so useful if you’re a graphic designer, who is likely to receive lots of useful emails that meet these parameters. As a result, machine learning allows the software to adapt to each user based on his or her own requirements. When the system flags some emails as spam, the user’s response to these emails (either reading or deleting them) will help train the AI agent to better deal with this kind of email in the future. Source