How Facebook uses Machine Learning

Today, as we all know data is the new oil. Most of the companies, if not all, have increasingly become data driven. It is the data through which companies earn. They build amazing applications which are very much personalized to a user with the help of Machine Learning, broadly speaking Artificial Intelligence.

Just to give you an idea what AI can do, let’s move to 2016. A UK based Company named DeepMinds which is acquired by Google developed an reinforcement learning based System named “Alpha Go” that was made to compete with the World Go Champion “Lee Sedol”. Many people thought that it was not possible for an AI Agent to beat a world champion.

But to the surprise, Alpha Go managed to make a clear win against “Lee Sedol” with 4–1 margin. That made a breakthrough in the field of AI.

Today, every company uses Machine Learning to improve their businesses. Today, we will see how Facebook uses Machine Learning to provide a personalized interface to each and every user. Facebook today has 2.41 billion monthly active users.

  1. Facebook Recognition

Ever thought how Facebook recognizes your friend in a pic. Behind the hood, it is Machine Learning which is doing this magic. Machine Learning System analyzes the pixels of the face in the image and creates a template which is basically a matrix of numbers.

2. Textual Analysis

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.

3. 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.

4. 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!

Hope you liked,

Thank you!!

Data Science, Big Data, Cloud Computing