Mainly in this era of machines, smart devices and applications are continuously becoming a routine phenomenon, thereby helping us make faster and more accurate decisions. It wouldn’t be a big surprise if we’d disclose the figure here. As more than 75% of businesses have already invested in Big Data, the role of AI and machine learning is all set to increase dramatically over the next five years.
As of 2017, the data that has been given after analysis shows that one-third of the organizations are spending 15% or more of their IT budget on machine learning capabilities, and thus we expect the number of machine learning examples to rise in the coming future.
But, do you wonder why this technology is called ‘machine learning’ the next must-have for competitive businesses? We consider you’ve been aware of only the tit-bits. So, why not delve into it to know its full potential?
Well, Machine learning is the latest approach to digital transformation, making our computing processes more efficient, cost-effective, and reliable. It is no longer just the fancy of science fiction writers, but a bona fide business-critical technology that will ultimately make decision-making a far more data-driven process. And through this, you will certainly be in a position to project your organization with better technology decisions.
Let’s get into the real-world examples where machine learning has been playing a major part!
Quite a famous platform that has gained much popularity in the last few months, PayPal uses machine learning algorithms to detect and combat fraud. By implementing deep learning techniques, PayPal can analyze the vast amount of customer data and evaluate risk in a far more efficient manner.
Traditionally, fraud detection algorithms have dealt with very linear results: fraud either has or hasn’t occurred. But with ML and neural networks, PayPal is able to draw upon financial and network information to provide a deeper understanding of a customer’s activity.
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Who doesn’t know about the very popular Netflix? Well, almost all the youths and the coming generations are fond of this media service provider. And, in fact, if you’d pay attention, more than 80% of the TV shows here are found through its recommendation engine. Now, this is where machine learning plays a very important part.
Mainly, there are two things that feed the network: user behavior and program content. Together, these datasets create multiple ‘taste groups’, which tell the recommendation engine to serve up those specific programs accordingly.
In the year 2017, Google put forward the concept of ML into Google Maps in order to improve the usability of the service. Here in Google Maps, it basically utilizes deep learning algorithms that help the app extract street names and house numbers from photos taken by Street View cars and increase the accuracy of the search results.
Through this, it has become easy as it frees up more time for Google engineers, automatically extracting information from geolocated images.
Ever upload your pictures and notice when Facebook prompts you to tag your friends? In fact, nowadays it even recognizes your friends’ names through their faces in the pictures! Isn’t it amazing? This happens due to machine learning as social network’s algorithms recognize familiar faces from your contact list, using some seriously impressive technology, which is DeepFace.
With the capability to recognize human faces with a 97.25% degree of accuracy, DeepFace has become adept at recognizing the nuances in human countenance across 4000 separate identities.
Do you get the feeling when you listened to that one cheesy hip-hop song and after its completion, another similar cheesy hip-hop song pops up? And not just that, based on this, you get further song recommendations…
Well, that’s how ML works. Much like Netflix, Spotify also uses robotics to figure out your likes & dislikes in order to provide you with a list of your related songs and all these are hand-picked by ML algorithms.
Existing businesses and companies continue to develop their processes by employing emerging technologies. There are more and more companies who are warming up to the idea of an automated future and what better than knowledge engineering? Moreover, industries that lack competition will benefit from the commercial advantages of this technology.
With most organizations currently in the early stages of machine learning adoption, there’s still plenty of time to set yourself up as a frontrunner in your own industry niche. Not only will this provide your business with an extensive competitive edge, but also it’ll completely redefine the way we think about work.