Machine Learning & Why it’s Important


Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.

So, machine learning is the science of getting computers to act “without being explicitly programmed”. But there is still a lot of programming behind getting computers to program themselves. Computers and their applications are being designed for change and adaptability. Programmers want their applications to respond to more than just intentional input.

Applications should be able to respond to external stimuli, big data, crowdsourced social activity, and a legion of other sources. What are some applications of machine learning? Machine learning is showing up in more areas than we might realize.


Machine Learning Around Us

Right now, on a roadway somewhere, a self-driving car is making right-hand turns (using its signal), braking (gently), and pausing for pedestrians (considerately).

The self-driving car is a prime example of machine learning put into motion, literally.

The self-driving car is heavily programmed, but not by itself. A small army of very intelligent people have spent a long time creating virtual reality maps, developing vehicular adaptations, and forming a new industry in order to help the car drive safely and reliably.

Yet, at the same time, the car does program itself.

The job of the [car’s] software is to figure out how the world is different from that expectation.

As the car figures it out, it adapts.


Machine Learning on Websites We Use

But machine learning isn’t usually as sexy as most of its applications. Right now, in a Facebook newsfeed near you, machine learning is controlling what you do, see, and interact with.

During Facebook’s tumultuous 2011, they switched from an algorithm called EdgeRank to a more complicated one. The machine learning of Facebook’s advanced newsfeed algorithm tries to individualize your Facebook experience based on what it thinks you want. You teach it with every linger, look, click, query, and interaction that you perform.

Here’s how Time explains it:

To ensure that those 300 posts are more interesting than all the rest, Facebook says it uses thousands of factors to determine what shows up in any individual user’s feed. The biggest influences are pretty obvious. How close you are to a person is an increasingly important metric, as judged by how often you like their posts, write on their Timeline, click through their photos or talk with them on Messenger, Facebook’s chat service. The post-type is also a big factor, as Facebook hopes to show more links to people who click lots of links, more videos to people who watch lots of videos and so forth. The algorithm also assumes that content that has attracted a lot of engagement has wide appeal and will place it in more people’s feeds.

When you clicked on Joe’s picture, or searched for “Joe B—” in your search bar, the machine-learning algorithm picked up on it. Tomorrow morning, when you open up Facebook on your phone, guess who’s updated profile picture will top your newsfeed?


It’s not quite that starkly cause-and-effect, but the principle remains true. Facebook’s newsfeed algorithm operates on machine learning.


Machine Learning in SEO

Machine learning is expanding everywhere. Although Google’s machine learning is focused on search improvement, they also advance machine learning in a whole breadth of applications. To say that machine learning is changing SEO is a bit anachronistic. Why?

Because machine learning is already a major part of SEO.

In fact, it has been for a long time.

Right now, however, it’s growing in importance.

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