If you are a Facebook user, you have probably noticed “suggested searches,” or been prompted by Facebook to friend certain people. Whether associated with things you have liked in the past or the number of mutual friends involved, these suggestions, for the most part, are fairly logical.

Facebook's applied machine learning is becoming society's big brother in the sky.  PHOTO VIA FLICKR USER KISS MY GRANDMOTHER

Facebook’s applied machine learning is becoming society’s big brother in the sky. PHOTO VIA FLICKR USER KISS MY GRANDMOTHER

Those suggestions are made possible thanks to a little trick known as applied machine learning. This means Facebook tracks your Facebook statuses, what you share, what you like and what you look up to get to know you and filter suggested searches based on its gathered data. Pretty impressive, right? As you can imagine, this process is fairly complex.

Damon Beres, editor of HuffPost Tech, recently wrote about his Facebook experience featuring such machine learning. The social networking site prompted him to search for his fiancé’s sister “licking batter” and his friend plus the word “nudist.” As it turned out, the latter term was related to a diorama the friend’s partner had made.

Beres discusses the ambiguity in the link between “licking batter” and “nudity,” which can probably be attributed to the amount of detail Facebook must focus on to filter searches. Then again, he explains, the search options could be based on previous data that Facebook acquired. Its ability to pair words and develop a suggestion through them is remarkable. It’s safe to say that Facebook knows its audience.

I can completely relate to Beres’s experience firsthand. For me, Facebook pretty much knows me like the back of its hand. It knows that I am a sucker for a good Buzzfeed quiz or for an article about a previously disabled animal that has been treated and is now happy-go-lucky.

In terms of videos, Facebook knows to show me any and all videos of cats or dogs (and occasionally, hedgehogs) being weird, people pulling funny pranks or three-ingredient recipes that I hope to cook up for my next meal. All of these examples can keep me entertained for hours. Plus, when I click on one video, it gives me even more related suggestions and naturally, I don’t want to pass up on the opportunity to watching another video of a cat jumping away from a cucumber.

With all of the above, I often brag about how I have the best Facebook newsfeed, but when I really think about it, it is a little startling how well Facebook, with its applied machine learning, knows me — and I mean really knows me, almost like a friend. It knows my likes and dislikes (thanks to the “hide” button), recognizes my acquaintances and even helps me set up my social calendar by linking me to events I might be interested in.

It is almost like a “Big Brother is watching” deal, ready to learn more about me any time I take action on its website. My mom has discussed George Orwell’s “1984” with me and she talked about how the concept of “Big Brother” in that book seemed so foreign when she read it in school. But now, it is not such a far-fetched idea.

Moreover, this applied machine learning can sometimes grow downright annoying. Sometimes I just want to scream, “Enough already! Stop taunting me with more videos!” With its constant search suggestions filtered specifically for me, Facebook can become quite addictive. That being said, if it were to suddenly disappear, I would not be too thrilled either. After all, scrolling onto Facebook to find mindless videos or articles to give my brain a rest is a nice part of my day.

And so, I guess I will stay pro-applied machine learning for now. However, I do hope it does not get too much more involved in social media as we know it.