What Is Machine Learning And How Is It Used Today?

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Machine learning is a phrase that’s getting bandied about increasingly often, yet many still don’t know exactly what it is. Of course, there’s a reason for that. It’s still in its very early stages, and many assume it’s not something that affects the general population just yet. In fact, that’s perhaps not as true as some assume.

So what is machine learning? And what is it being used in today? Here’s our guide on everything you need to know about machine learning.

What Is Machine Learning?

Machine learning, simply put, is a form of artificial intelligence that allows computers to learn without any extra programming. In other words, the software is able to learn new things on its own, without a programmer or engineer needing to ‘teach’ it anything. Machine learning is able to take data and detect patterns and find solutions, then applying those solutions to other problems.

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It’s important to note that machine learning as a concept isn’t new at all — it’s hard to trace the precise origins of the concept considering it’s one that merges into and from other forms of technology. You could argue that machine learning dates all the way back to the creation of the Turing Test, which was used to determine if a computer had intelligence. The first computer program that learning, however, was a game of checkers, which was developed in 1952 by Arthur Samuel. This game got better the more it played.

Recent technology, however, drastically improves machine learning. For example, machine learning requires hug amounts of processing power, so much so that we’ve only just started being able to develop basic machine learning in recent history.

There are a few main ways programmers implement machine learning. The first is called ‘supervised learning.’ What that basically means is that a machine is fed problems where the solution to the problem is known. The learning algorithm is able to receive those problems along with the desired outcomes, identifying patterns in the problems and acting accordingly. Supervised learning is often used to predict future events — such as when a credit card transaction might be fraudulent.

The second implementation of machine learning is is called ‘unsupervised learning.’ In this instance, the outcome of a problem isn’t given to the software — instead, it’s fed problems and has to detect patterns in the data. The goal here is to find a structure in the data that it’s given.

Third up is ‘semi-supervised learning.’ This method of machine learning is often used for the same things as supervised learning, but it takes data with a solution and data without. Semi-supervised learning is often implemented when funds are limited and companies are unable to provide full sets of data for the learning process.

Last but not least is ‘reinforcement learning,’ which is used specifically for things like gaming and robots. Reinforcement learning is basically taught through trial and error — the machine attempts things and learns based on its successes or failures. The goal here is for the machine to figure out the best possible outcomes.

Of course, all of these methods of machine learning involve feeding a machine hundreds of thousands of problems, and massive amounts of data. Really, the more data the better.

Where Is Machine Learning Used Today?

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Actually, there are plenty of places in which machine learning is used today. Many of these are behind the scenes, however you may be surprised to know that a lot of them are also something that you use every single day.

Perhaps the one that you use the most is in your personal assistant — that’s right, the likes of Siri and Google Now use machine learning, largely to better understand speech patterns. With so many millions of people using Siri, the system is able to seriously advance in how it treats languages, accents, and so on.

Of course, Siri isn’t the only consumer application of machine learning. Another use is is in banking, such as fraud detection. For example, machine learning algorithms can track spending patterns, determining which patterns are more likely to be fraudulent based on past fraudulent activity.

In fact, even your email might be using machine learning. For example, spam emails are a problem, and they have evolved over time. Email systems use machine learning to track spam email patterns and how spam emails change, then putting them in your spam folder based on those changes.

Conclusions

Machine learning is set to be a big part of how we use technology going forward, and how technology can help us. From Siri to US Bank, machine learning is becoming increasingly pervasive, and that’s only likely to continue.

Image courtesy of Full Coverage Insurance.