How does Google Use Machine Learning?

By NIIT Editorial

Published on 17/01/2021

6 minutes

Google’s predilection for machine learning is evident from open source initiatives that it has undertaken to promulgate Machine Learning as an emerging technology. TensorFlow and the Brain Residency Program are a testament to it. Frankly speaking, if a company the size of Google doesn’t make use of machine learning, and encourages others to do the same with its publicly declared love for the same, then no one would. As a matter of fact, Google effectuates over 3.5 billion searches every day and claims the top spot as the leading search engine. With enough data to exercise machine learning to its capable self, here are some applications that Google implements high-end machine learning operations with. 


Google Translate 


A respite for the travellers moving across locations and not knowing the local tongue, Google Translate uses character recognition to help you understand foreign languages. Its multi-faceted specifications allow users to input images, text, and even real-time videos. Having scanned through millions of documents posted in the internet, the algorithm continues to learn and expand its memory to best facilitate people. 


A study found that Google translate was able to convey the underlying meaning of the captured text, more than 50% of the time for 35 languages. Beginning with statistical machine translation, Google has shifted to a neural machine translation mechanism for higher accuracy. Long story short, the new technique uses predictive algorithms for translation. 


Google Voice Search 


Google has integrated this feature in most of its subsidiary businesses such as YouTube and android based smart devices. Experts tout it as a move take on Apple’s Siri with user reviews validating the claims that Google is better at voice search than Apple. The feature uses natural language processing. It helps the voice search function figure not just the literal meaning of the voice command, but also its implied meaning and implications. In 2012, Google switched to using Deep Neural Networks (DNN) so it could analyse sounds at the very instant they were produced. DNNs have multiplied voice search efficiency and made the feature more accurate. 




Just when people thought the email was dying Google assigned its machine learning and artificial intelligence experts to the task and reinvented it. Singling out some of the most common email replies that people use, it introduced a Smart Reply feature that allowed users to respond instantly from a list of pre-selected options. It is reported that about 10% of all email responses on the mobile are essentially Google’s doing vis-a-vis the Smart Reply. Being launched in 2015, Google’s algorithm can complete your sentences, as you write them with its auto-comment picker. 




This is the algorithm that supervises the top-ranking links on Google’s Search Engine Results Page (SERP). In the pilot years of RankBrain, it was used for only 15% of the searches conducted on Google. But after gaining confidence, it deployed RankBrain as a query refinement yardstick to not just identify relevant results but also define their order of appearance. 


The machine-learning algorithm of RankBrain has helped Google connect content with context. Its release has helped Google understand the user intent behind a search query. The guiding principle of RankBrain is simple, it correlates the patterns behind a particular search keyword by associating it with other keywords. 


Google Photos  


Google Photos has been remarkably efficient in handling cloud storage operations smartly. And if the users weren’t enamoured enough with the app owing to its unlimited cloud storage for photos, they would certainly be drawn to it by its AI-powered features. Google Photos can be accessed across devices with an internet connection and is far ahead of competitor apps such as Apple’s iCloud, Amazon’s Cloud Drive, and Microsoft’s OneDrive. The app uses convolutional neural networks (CNNs). 


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