How Facebook and Google uses Machine Learning at their best
By: Shivam Prasad Upadhyay
Tags: Machine Learning, Speech Recognition, Facebook, Google, Facial Recognition

“Machine learning will automate jobs that most people thought could only be done by people.” ~Dave Waters

Hello everyone , so today I will like to tell you how the most famous companies Facebook and Google uses Machine Learning at their best to ease their tasks and do cool stuffs that earlier was thought to be impossible.

What is Machine Learning?

Have you ever wondered that how we learn or how from our birth our brain learns each and every bit, be it our parents and friend’s faces or riding bicycles or learning mathematical formulas etc. It is because our brain observes whatever we see and makes patterns (which we call as experiences )and by analysing these patterns it predicts and makes further decisions. Due to this decision making power of our brain unlike computers the developers thought that why not we give prediction and decision making power to the computers which would add extra stars to the computers speed ie now computers can take decisions and do predictions as fast as they do calculations. So to achieve this they brought the concept of Machine learning which is to make some programs that based on the data provided to the computers (similar to experiences of our brain) can predict results or target values. In this way these programs can basically help machines learn.

“Machine intelligence is the last invention that humanity will ever need to make.” ~Nick Bostrom

Now talking about the companies, Google and Facebook took a great advantage of the Machine Learning and not only reduced their work load that was earlier done by humans but also proved to be the smartest and most lucrative and innovative companies for the clients.

How Google uses Machine Learning?

Google has declared itself a machine learning-first company.

Google is the master of all. It takes advantage of machine learning algorithms and provides customers with a valuable and personalized experience. Machine learning is already embedded in its services like Gmail, Google Search and Google Maps.

Google services, for example, the image search and translation tools use sophisticated machine learning. This allows the computer to see, listen and speak in much the same way as humans do. Much wow!

Gmail

As you all know that our social, promotional and primary mails are separated in different boxes . This is filtered through Google as it labels the email accordingly. This is where machine learning plays a crucial part. The user intervention is used to tune to its threshold and when a user marks a message in a consistent direction, Gmail itself performs a real-time increment to its threshold and that’s how Gmail learns for future and later uses those results for categorization.

Smart replies:

This is really a smart move made by Google. Now, with the help of this feature, you can reply instantly in a second. With the suggested replies given by Gmail. ‘Smart Replies’ and ‘Smart Compose’ are indeed the best products that Google has given to its customers. These are powered by machine learning and will offer suggestions as you type. This is also a major reason why Google stands as one of the leading companies today.

Also, it is not just in English. It will bring support in four new languages: Spanish, French, Italian and Portuguese.

Google Search and Google Maps

This also employs machine learning and while you start typing in the search box it automatically anticipates what you are looking for. It then provides suggested search terms for the same. These suggestions are showcased because of past searches (Recommendations), trend (which everyone is looking for), or from your present location.

For example — Bus traffic delays — Hundreds of major cities around the world, thousands of people traveling, One machine that is learning and informing . Google gets all the real-time data on bus locations and forecasts it in a jiffy. So, now you don’t have to wait long hours for your bus.

With the combination of time, distance traveled, and individual events as datasets, it is now possible for Google to provide predictions. Now, there is no need to rely on bus schedules provided by public transportation agencies.

With the help of your location, day of the week, and time of day, your estimated time of arrival (ETA) can be understood.

The best invention ever done for students is Google search and no one will deny it. Google Search and Google Maps use machine learning too and help the people in their day to day tasks.

You can check the awesomeness of Google Machine Learning by just going to Google and then typing or speaking just weather and without asking anything it will automatically tell you the whole weather report for your area. This is the cause of Machine Learning

Google Assistant

It helps one to assist in everyday tasks, be it household chores or a deal worth crores. The Google Assistant makes it easy for you to search for nearby restaurants when it’s raining heavily, helps you to buy movie tickets while on the go and find the nearest theatre from your place. Also, helps you to navigate to the theater. In short, you don’t have to worry when you have a smartphone, because Google takes care of everything. This is all done due to strong machine learning algorithms used by google.

Google Translate

The world is migrating. Leave the rest of the world, at least 24 languages are spoken in India itself, with over 13 different scripts and 720 dialects. Well, if we talk about the world there are roughly 6,500 spoken languages in the world today. Can’t thank Google enough cause we’ve all used Google Translate at some point (I hope, you travel a lot too).The best is that it’s free, fast, and is generally accurate. Its translation of words, sentences, and paragraphs have helped many to decode and understand.

It is true that it is not 100% accurate when it comes to larger blocks of text or for some language, but it can provide people with a general meaning to make the understanding less complex. All this is possible because of Statistical Machine Translation (SMT). So, no matter how much you hate mathematics or statistics you will have to thank and love it .

This is a process where computers analyze millions of existing translated documents from the web to learn vocabulary and look for patterns in a language. After that Google translates it. It then picks the most statistically probable translation when asked to translate a new bit of text.

Speech Recognition: Ok, Google.

The speech recognition feature enables the user to convert audio to text by applying powerful neural network models in an easy-to-use API. Currently, the API recognizes 120 languages and its variants to support the global user base. Through this voice, command-and-control can be enabled and the audio can be transcribed from the call centers. Also, the processing of real-time data can be done. Starting from streaming to prerecorded audio, speech recognition has mastered it all and all credits can be given to Google’s machine learning technology.

Reverse Image Search

Google’s Search by Image is a feature that uses reverse image search and allows users to search for related images just by uploading an image or image URL. Google accomplishes this by analyzing the submitted picture and constructing a mathematical model of it using advanced algorithms. It is then compared with billions of other images in Google’s databases before returning matching and similar results. When available, Google also uses metadata about the image such as description. Reverse image search is a content-based image retrieval (CBIR) query technique that involves providing the CBIR system with a sample image that it will then base its search upon; in terms of information retrieval, the sample image is what formulates a search query.

Image search creates categories that you might be looking for. With the image search, it becomes easy to search for similar images. It also helps to find the websites that contain these images and the other sizes of the picture you searched with.

Google Adsense

With the help of machine learning, Google keeps track of the users’ search history. With the help of that history, it recommends the advertisement to the user as now its aware of its target market. It’s heavily based on the search history data and machine learning helps Google to achieve this.

It created a win-win situation. With Google AdSense, the website owners earn money from their online content and AdSense works by matching text and display ads to the site based on the content and the visitors.

There are many more examples such as Google Music, Google Photos ,Google Adwords etc. which makes great use of Machine Learning and that is the reason that Google has the most number of users in most of the field as it not only makes our work easier but also solves the problem smartly.

How Facebook uses Machine Learning?

Machine Learning is the vital aspect of Facebook. It would not even be possible to handle 2.4 billion users while providing them the best service without using Machine Learning!

Let’s take an example. It is mind-boggling how Facebook can guess the people you might be familiar with in real life using “People You May Know”. And they are right most of the time!!! Well, this magical effect is achieved by using Machine Learning algorithms that analyze your profile, your interests, your current friends and also their friends and various other factors to calculate the people you might potentially know. That’s only one example ,other aspects are the Facebook News Feed, Facial Recognition system, Targeted Advertising on your page, etc. which we would look below.

Facial Recognition

Facial Recognition is among the many wonders of Machine Learning on Facebook. It might be trivial for you to recognize your friends on social media (even under that thick layer of makeup!!!) but how does Facebook manage it? Well, if you have your “tag suggestions” or “face recognition” turned on in Facebook (this means you have provided permission for Facial Recognition), then the Machine Learning System analyses the pixels of the face in the image and creates a template which is basically a string of numbers. But this template is unique for every face (sort of a facial fingerprint!) and can be used to detect that face again in another face and suggest a tag.

So now the question is, What is the use of enabling Facial Recognition on Facebook? Well, in case any newly uploaded photo or video on Facebook includes your face but you haven’t been tagged, the Facial Recognition algorithm can recognize your template and send you a notification. Also, if another user tries to upload your picture as their Facebook profile picture (maybe to get more popular!), then you can be notified immediately. Facial Recognition in conjugation with other accessibility options can also inform people with visual impairments if they are in a photo or video.

Textual Analysis

While you may believe photos are the most important on Facebook (especially your photos!), the text is equally as important. And there is a lot of text on Facebook!!! To understand and manage this text in the correct manner, Facebook uses DeepText which is a text engine based on deep learning that can understand thousands of posts in a second in more than 20 languages with as much accuracy as you can!

But understanding a language-based text is not that easy as you think! In order to truly understand the text, DeepText has to understand many things like grammar, idioms, slang words, context, etc. For example: If there is a sentence “I love Apple” in a post, then does the writer mean the fruit or the company? Most probably it is the company (Except for Android users!) but it really depends on the context and DeepText has to learn this. Because of these complexities, and that too in multiple languages, DeepText uses Deep Learning and therefore it handles labeled data much more efficiently than traditional Natural Language Processing models.

Targeted Advertising

Did you just shop for some great clothes at Myntra and then saw their ads on your Facebook page? Or did you just like a post by Lakme and then magically see their ad also? Well, this magic is done using deep neural networks that analyze your age, gender, location, page likes, interests, and even your mobile data to profile you into select categories and then show you ads specifically targeted towards these categories. Facebook also partners with different data collection companies like Epsilon, Acxiom, Datalogix, BlueKai, etc. and also uses their data about you to accurately profile you.

For Example, Suppose that the data collected from your online interests, field of study, shopping history, restaurant choices, etc. profiles you in the category of young fashionista according to the Facebook deep neural networks algorithm. Then the ads you are shown will likely cater to this category so that you get the most relevant and useful ads that you are most likely to click. (So that Facebook generates more revenue of course!) In this way, Facebook hopes to maintain a competitive edge against other high-tech companies like Google who is also fighting to obtain our short attention spans!!!

Language Translation

Facebook is less a social networking site and more a worldwide obsession! There are people all over the world that use Facebook but many of them also don’t know English. So what should you do if you want to use Facebook but you only know Hindi? Never fear! Facebook has an in-house translator that simply converts the text from one language to another by clicking the “See Translation” button. And in case you wonder how it translates more or less accurately, well Facebook Translator uses Machine Learning of course!

The first click on the “See Translation” button for some text (Suppose it’s Beyonce’s posts) sends a translation request to the server and then that translation is cached by the server for other users (Who also require translation for Beyonce’s posts in this example). The Facebook translator accomplishes this by analyzing millions of documents that are already translated from one language to another and then looking for the common patterns and basic vocabulary of the language. After that, it picks the most accurate translation possible based on educated guesses that mostly turn out to be correct. For now, all languages are updated monthly so that the ML system is up to date on new slangs and sayings!

News Feed

The Facebook News Feed was one addition that everybody hated initially but now everybody loves!!! And if you are wondering why some stories show up higher in your Facebook News Feed and some are not even displayed, well here is how it works! Different photos, videos, articles, links or updates from your friends, family or businesses you like show up in your personal Facebook News Feed according to a complex system of ranking that is managed by a Machine Learning algorithm.

The rank of anything that appears in your News Feed is decided on three factors. Your friends, family, public figures or businesses that you interact with a lot are given top priority. Your feed is also customized according to the type of content you like (Movies, Books, Fashion, Video games, etc.) Also, posts that are quite popular on Facebook with lots of likes, comments and shares have a higher chance of appearing on your Facebook News Feed.

So theses are some of the cool stuffs about how theses large companies are benefitted by Machine Learning. So now I will take your leave by telling you some interesting words of the co-founder of Chatables , Amy Stapleton and director of Paetoro Dr. Dave Waters.

We are entering a new world. The technologies of machine learning, speech recognition, and natural language understanding are reaching a nexus of capability. The end result is that we’ll soon have artificially intelligent assistants to help us in every aspect of our lives.” ~Amy Stapleton Predicting the future isn’t magic, it’s artificial intelligence.” ~Dave Waters

Thank you for reading!!