How Facebook and Google uses Machine Learning at their best

“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 use Machine Learning at their best to ease their tasks and do cool stuff that earlier was thought to be impossible.

Have you ever wondered that how we learn or how from our birth our brain learns each and every bit, be it our parent's 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 analyzing 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 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 great advantage of Machine Learning and not only reduced their workload that was earlier done by humans but also proved to be the smartest and most lucrative and innovative companies for the clients.

How does Google use Machine Learning?

  • Google has declared itself to be a firm that prioritizes machine learning.
  • Google is the king of them all. It makes use of machine learning algorithms to create a valuable and tailored experience for clients. Google has already incorporated machine learning into its services such as Gmail, Google Search, and Google Maps.
  • Google’s picture search and translation features, for example, use advanced machine learning. This enables the computer to see, hear, and communicate in a manner similar to that of humans. Much adulation!

Our social, promotional, and primary mails are all segregated into different boxes, as you are aware. This is filtered by Google because the email is labeled accordingly. This is where machine learning comes into play. When a user marks a message in a consistent direction, Gmail executes a real-time increase to its threshold, which is how Gmail learns for the future and later uses those results for categorization.

Smart replies:

This is a brilliant move on Google’s part. You may now respond in a split second with the help of this feature. With the help of Gmail’s suggested responses. ‘Smart Replies’ and ‘Smart Compose’ are two of Google’s most useful features for its users. Machine learning is used to power these, and they will make suggestions as you write. This is also one of the main reasons why Google is one of the most successful firms today.
It’s also not simply in English. It will add four new languages to the mix: Spanish, French, Italian, and Portuguese.

This also uses machine learning, and it anticipates what you’re looking for as you start entering the search field. Following that, it suggests search terms for the same. These recommendations are highlighted as a result of previous searches (Recommendations), a trend (which everyone is looking for), or your current location.

For instance, consider bus traffic delays. Hundreds of major cities throughout the world, thousands of individuals on the go, and one learning and informing machine. Google quickly gathers all of the real-time data on bus locations and forecasts it. As a result, you will no longer have to wait for your bus for several hours.
Google can now make predictions based on a combination of time, distance traveled, and particular occurrences as datasets. There is no longer any need to rely on public transit organizations’ bus schedules.
Your estimated time of arrival (ETA) can be calculated using your location, day of the week, and time of day.

No one can deny that Google search is the best innovation ever made for students. Machine learning is also used by Google Search and Google Maps to assist individuals with their daily duties.

You can test Google Machine Learning’s brilliance by going to Google and typing or saying only weather, and it will automatically tell you the entire weather report for your area without you having to ask anything. Machine Learning is the result of this.

It enables one to assist in everyday duties, whether they are domestic chores or a multimillion-dollar deal. When it’s raining hard, the Google Assistant makes it simple to identify nearby eateries, buy movie tickets on the fly, and locate the closest theatre to your location. It also assists you in finding your way to the theatre. In short, when you have a smartphone, you don’t have to worry about anything because Google takes care of everything. All of this is possible because of Google’s powerful machine learning algorithms.

The entire planet is relocating. Leaving aside the rest of the globe, India has at least 24 languages, with around 13 distinct scripts and 720 dialects. If we talk about the world now, there are approximately 6,500 spoken languages. We’ve all used Google Translate at some point, so we can’t thank them enough (I hope, you travel a lot too). The best part is that it’s completely free, quick, and accurate. Many people have benefited from its translation of words, phrases, and paragraphs.

True, it is not 100 percent correct when it comes to huge blocks of text or some languages, but it can provide users with general knowledge to make comprehension easier. Statistical Machine Translation makes all of this possible (SMT). So, no matter how much you despise mathematics or statistics, you must thank and admire it.

This is a method of learning vocabulary and looking for patterns in a language by analyzing millions of existing translated papers from the internet. After that, Google will translate it for you. When requested to translate a new piece of text, it chooses the most statistically likely translation.

The speech recognition capability allows users to convert audio to text using a simple API and strong neural network models. To support the global user base, the API now recognizes 120 languages and their variants. Command-and-control can be activated with this voice, and audio from contact centers can be transcribed. It is also possible to process real-time data. Speech recognition has mastered everything from streaming to prerecorded audio, and all credit goes to Google’s machine learning engine.

Google’s Search by Image is a reverse image search service that allows users to find related photos simply by submitting an image or URL. Google achieves this by evaluating the submitted image and applying powerful algorithms to create a mathematical model of it. The image is then compared against billions of other photographs in Google’s databases before matching and similar results are returned. When accessible, Google additionally takes advantage of image metadata such as the description. The sample picture is what formulates a search query in terms of information retrieval. Reverse image search is a content-based image retrieval (CBIR) query strategy that entails providing the CBIR system with a sample image on which it will then base its search.

Image search generates categories that you may be interested in. It is simple to find similar photographs using the image search. It also aids in the discovery of websites that have these photographs, as well as other sizes of the image you sought.

Google uses machine learning to keep track of its users’ search histories. It offers the advertisement to the user based on that history because it is now aware of its target market. It is mainly based on search history data, with machine learning assisting Google in this endeavor.
It resulted in a win-win scenario. Website owners can make money from their online content using Google AdSense, which works by matching text and display adverts to the site based on the content and visitors.

There are many more examples, such as Google Music, Google Photos, Google Adwords, and so on, that make extensive use of Machine Learning, which is why Google has the largest user base in almost every industry because it not only makes our work easier but also solves problems intelligently.

How Facebook uses Machine Learning?

Facebook relies heavily on machine learning. Without Machine Learning, it would be impossible to accommodate 2.4 billion consumers while offering the best possible service!
Let’s have a look at an example. It’s incredible how Facebook’s “People You May Know” feature can predict who you could know in real life. And, for the most part, they are correct!!! This magical effect is achieved through the use of Machine Learning algorithms that examine your profile, your hobbies, your current friends and their friends, and a variety of other characteristics to determine who you could know. That’s just one example; we’ll examine at the Facebook News Feed, the Facial Recognition system, and Targeted Advertising on your page, among other things, further down.

On Facebook, facial recognition is one of the many marvels of Machine Learning. You might not be able to recognize your buddies on social media (even if you’re wearing a thick layer of makeup!!!). However, how does Facebook deal with it? If you have “tag suggestions” or “facial recognition” set on on Facebook (which means you have given Facial Recognition permission), the Machine Learning System analyses the pixels of the face in the image and develops a template, which is just a string of numbers. However, because this template is unique to each face (kind of like a facial fingerprint! ), it may be used to recognize that face in another and recommend 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.

While you may assume that images (especially your own) are the most significant aspect of Facebook, text is just as vital. On Facebook, there is a lot of text!!! Facebook utilises DeepText, a deep learning-based text engine that can comprehend thousands of posts in a second in more than 20 languages with as much accuracy as you can!

However, deciphering a material written in a foreign language is not as simple as you might believe! DeepText must understand numerous factors in order to really comprehend the text, including syntax, idioms, slang phrases, context, and so on. For example, does the writer mean the fruit or the firm when he or she says “I love Apple” in a post? It’s almost certainly the firm (except for Android users!). However, it is dependent on the context, which DeepText must learn. DeepText employs Deep Learning to deal with this complexity, and it does so in several languages. As a result, it handles labeled data far more effectively than typical Natural Language Processing models.

Have you recently purchased some fantastic clothing from Myntra and then noticed their adverts on your Facebook page? Or did you just like a Lakme post and then see their ad as well? Deep neural networks scan your age, gender, location, page likes, interests, and even mobile data to profile you into certain groups, and then display your advertising that is specially targeted to these categories. Facebook also works with firms like Epsilon, Acxiom, Datalogix, BlueKai, and others to collect data about you and utilize it to precisely profile you.

For example, suppose Facebook’s deep neural networks algorithm categorizes you as a young fashionista based on data collected from your online interests, the field of study, purchasing history, restaurant preferences, and so on. The advertising you see will most likely be tailored to this category, so you’ll see the most relevant and useful ads that you’re most likely to click. (Of course, so that Facebook can make more money!) In this way, Facebook intends to maintain a competitive advantage against other high-tech corporations, such as Google, who are also vying for our limited attention spans!!!

Facebook is more of a worldwide obsession than a social networking platform! People from all around the world use Facebook, but many of them do not speak English. So, if you want to utilize Facebook but only speak Hindi, what should you do? Do not be concerned! By pressing the “See Translation” option, Facebook’s in-house translator transforms the content from one language to another. And, in case you’re wondering how it manages to translate more or less accurately, Facebook Translator, of course, uses Machine Learning!

The first time you click the “See Translation” button for some text (let’s say it’s Beyonce’s posts), the server makes a translation request to the server, which the server caches for other users (who, in this case, also need translation for Beyonce’s posts). The Facebook translator achieves this by examining millions of texts that have already been translated from one language to another, then searching for common patterns and core terminology. Then, based on informed assumptions that are mostly true, it chooses the most accurate translation feasible. For the time being, all languages are updated once a month to keep the ML system up to date with new slang and phrases.

The Facebook News Feed was a feature that everyone initially despised, but now everyone adores!!! And if you’re curious about why some stories appear higher in your Facebook News Feed while others aren’t, here’s how it works! Various photographs, videos, articles, links, or updates from your friends, family, or favorite businesses appear in your personal Facebook News Feed based on a complicated ranking system governed by a Machine Learning algorithm.

Three criteria determine the order in which items display in your News Feed. Friends, relatives, public figures, and corporations with whom you have frequent contact are given top attention. Your feed is also tailored to the type of information you enjoy (Movies, Books, Fashion, Video games, etc.) Also, postings with a lot of likes, comments, and shares on Facebook have a better chance of showing up in your Facebook News Feed.

So those are some of the great things about how Machine Learning benefits these big companies. So now I’ll bid you farewell by sharing some insightful remarks from Amy Stapleton, co-founder of Chatables, and Dr. D. Paetoro, director of Pietro.

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!!

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Learner, Tech Enthusiast