Day-to-day machine learning applications you’re too busy to notice

Artificial intelligence is creating a splash everywhere you look. With all the hype and excitement surrounding AI — from instant machine translations to self-driving vehicles — it often becomes difficult to see how AI is already influencing our daily lives. The truth is, most of us use AI one way or another without realizing it. One example: machine learning applications, whereby software and hardware function through cognition. In the process, they imitate the functions of the human brain and get one step closer to infiltrating our day-to-day activities.

So, whether you’re in awe of the rise of machine learning or are gearing up for the inevitable AI rebellion, now might be a good time to keep your eyes peeled for some machine language applications in our everyday lives:

Video surveillance

Machine Learning Applications

Hiring a security person just to sit and stare at multiple screens no longer makes sense. After all, what guarantee is there that they will constantly pay the same amount of attention to each screen? In addition, there is the occasional food and bathroom break. Therefore, to prevent something from going unnoticed, computers are now trained to take over this role. Perhaps they can even help us detect when the Patriots will cheat again!

With the help of AI, video surveillance systems now detect crimes before they are even committed. Any unusual behavior, from stumbling to yelling, standing still for a prolonged period or napping on benches, can alert human attendants to come and take the necessary action.

Plus, once the suspicious activities are reported and help deter a serious situation, they improve the quality of the surveillance. Thus, machine learning is the perfect fit for backend tasks like these.

Ride-sharing apps

machine learning applications
Flickr / Mark Warner

Believe it or not, every time you hire a Lyft or Uber service, the price is calculated not by humans but by machine learning. In fact, this technology reduces the wait time and matches you with other passengers on the same route to decrease detours and to save the company money.

Uber has admitted to using machine learning for predicting rider demand and applying surge pricing. At the same time, the ride service also harnesses the power of machine learning to establish ride ETAs, calculate the right pickup location, and detect fraud. Uber Eats also uses machine learning to estimate the meal delivery window.

Can it make a pumpkin pie? That is what we want to know!

IT support desks

As the number of gadgets and devices increase, so do the number of people who require IT help desk support. The job requirements demand a switched-on individual, and in return, promise a great compensation package, diverse work, and a strong culture. But the enthusiasm of the staff is bogged down by the inability of the systems to match up.

For example, hundreds of remote employees attempt to access the main system, which fails to respond quickly enough. This leads to a large number of help desk tickets, all asking the same question regarding access. So, a solution is needed to sort out the underlying issue before the businesses lose productivity.

And this solution presents itself in the form of machine intelligence. Thanks to machine learning applications, the systems are always on high alert and respond at a moment’s notice. Moreover, if something unexpected happens, ML-powered applications can alter their responses at a moment’s notice and decide upon the best course of action.

If that’s not all, an AI service desk is on 24/7, serving the users without feeling frustrated or tired. Since it is capable of communicating like a normal person, it can learn the corrective measures and advise the supervisor about the proper way to go about it.

Best of all, ML frees the experienced, knowledgeable IT staff from wasting their talents on routine, boring tasks, and frees them up to bring more value to the business — like creating another type of treatment for an illness or another way to drill into a mountain for rock or minerals, for instance.

Social media

Social media networks are rife with machine learning applications, and you will be hard-pressed to find one aspect that does not involve the use of intelligence (or the one that does not sell you out to a third party, but that is another topic). For example, Instagram, one of the most popular social networks, uses ML to identify the contextual meaning of emoji.

Using algorithms to detect the sentiments behind emojis, Instagram creates its own set of emoji hashtags and auto-suggest emojis.

While this seems like a waste of ML on the surface, Instagram has experienced a surge in emoji usage across all demographics of late. Being able to understand and analyze the trend at a grand scale through this emoji-to-text translation establishes the basis for further analysis regarding how Instagram will be used in the future.

Online product recommendation

You might have looked up a particular item online a week ago. Afterward, you received numerous emails containing the same shopping suggestion. What’s more, online shopping apps and sites often recommend goods that match your interest and taste. But being pestered with emails is not that appealing and fighting off a business that wants you to perhaps buy something you don’t need when you have enough bills to pay is nothing to brag about.

Moreover, while there is no denying that this takes the shopping experience to the next level (as long as people are not annoyed or duped as just stated), it is also because of machine learning applications. Product recommendations are made after carefully studying how you interact with apps and websites, your brand preferences, items added or liked to the cart or past purchases.

We all know we regret ever buying “The Force Awakens,” “Kill Bill,” “Meet the Parents II,” or “Captain America I” DVDs, damn those movies were weak.

Email malware and spam filtering

machine learning applications

More than 325,000 malwares are found on a daily basis, and each code resembles its predecessor by almost 90 percent to 98 percent. The email security systems powered by ML recognize the coding pattern, and detect new malware with 2 percent to 10 percent variation without any hassle, thereby serving as an active layer of defense.

And that’s not all. There are various spam filtering approaches used by email clients. To ensure that these spam filters are updated continuously, machine learning-powered techniques are employed, including C 4.5 Decision Tree Induction and Multi-Layer Perceptron.

Machine learning applications: You probably used one today

As the use of AI continues to spread, it becomes harder to separate the machine learning applications from the regular applications. For example, you could have used machine learning at work, while online shopping, conducting an online search, or speaking to your friends online.

In fact, everything you did to navigate to these words onscreen might have been because of ML involvement. So where does it all stop? Given that machine learning is the scientific method currently used for constructing AI, it makes sense to monitor the latest developments in ML and map the trajectory of AI advancement.

Featured image: Flickr / Intel Free Press

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