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These days we would hardly find any enterprise which is not utilizing the power of Machine Learning (ML) or Artificial Intelligence (AI). Every business has now got activities driven by AI to revolve around. Day by day organizations are becoming dependent and automating tasks for efficient results and high accuracy. AI achieves this accuracy with the help of deep learning algorithms. For instance, you can have a look at the interactions we do with Alexa or Google search. These are all based on deep learning algorithms. And these keep on getting more accurate and relevant as the time proceeds i.e. the more we interact. ML further enhances the scenarios and help to make the most of every products and services. With ML, we can automate any analytical model building, based on which results would be driven.
There are various real-life machine learning based examples we come across every day. So, let's have a look at how these works and help us ease our work.
We are already familiar with how greatly Google is showcasing its ML products in action with Google Assistant and Google Camera to the world. But now it has extended it to Gmail and Google Photos too. Gmail has now got smart reply feature which will suggest small brief responses to whatever email you've received based on the content that is present in the email. The smart compose option will give you suggestions like greetings, closings, or some whole sentences in between while you're busy typing the email.
At Netflix, ML has been constantly used to improvise the recommendations and personalization problems. ML has also expanded into various other streams like content promotions, price modeling, content delivery and marketing too. The entire platform seems to run 80% through the recommendation engine. The neural network keeps a track on user behavior and program content. This is further merged up to create multiple taste groups on which the recommendation engine works.
ML is a fundamental part of this tech giant. From estimating the time to determining how far your cab is from your given location, everything is driven by ML. It uses algorithms to determine all these effectively. It does these by analyzing the data from the previous trips and putting it in the present situation. Even the other branch of this giant i.e. UberEATS, does the same. It takes into account various factors like food preparation time to estimate the delivery time.
These voice recognition systems are purely based on ML. Deep neural networks are also a part of these famous voice recognition systems. They are being trained in a such a way that they can imitate human interactions in exactly the same manner. As the interactions proceed, these apps will learn to understand the skeleton and grammar of the language. With some famous slangs, these can automatically get triggered with some pre-recorded responses from the system.
Ever played one song and got some five random songs which you would have never discovered by yourself? That's the beauty of this application named Spotify.
Much like how Netflix uses ML, Spotify uses it the same way. With the weekly releases, it gives you a list of around 30 songs, which you should listen to. It would directly make it as a playlist and send it to users. All of these songs are picked up by ML algorithms which analyze your activities and matches with your taste from the songs you have listened to in the past.
