What is Machine Learning/Artificial intelligence and how big companies like facebook using it and the challenges faced.

What is Machine Learning ?👲

Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.

🍤Machine Learning has different different definitions from different different universities/places but all means same in one or other way. some examples are below to understand more clearly that what is machine learning and what it do:🍤

1. “Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.” — Nvidia
2. “Machine learning is the science of getting computers to act without being explicitly programmed.” — Stanford
3. “Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”- McKinsey & Co.
4. “Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.” — University of Washington
5. “The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?” — Carnegie Mellon University
6. “Machine learning (ML) can be thought of as a way to recognize and draw conclusions from connections among data.” — facebook

🔥Yes, the data which was thought to be of no use is becoming wealth to the companies as this is the basic need of ML🔥.

There are 1000s of companies either small or big which have been benifited using machine learning in fact get boomed only because of this.

✨we can see machine learning use cases not in industries but in our day-to-day life also like recommendations on amazon and youtube, Amazon alexa,siri,etc, google translate, etc✨.

one of them is facebook also whose implementation of ML/AI we can feel in our daily life as discussed below.

1. 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.

2. 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.

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. 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!!!

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!

5. 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.

But, there are lots of concerning challenges also to these companies like some discussed below👇

A fight against misinformation

👉Misinformation is the biggest problem of Machine Learning as it trains or we can say machine learn wrong things and hence predicts or works wrongly. Members of the News Feed team at Facebook discuss the complexity of the problems they need to solve as they have to continuously to identify and remove misleading stories from the platform. The solutions require a deep commitment to changing Facebook for the better; empowering members of our global community to let us know when they see false info; and machine learning at an enormous scale.

Optimizing 360-degree photos at scale

Several technologies were developed and deployed in the past year to optimize the way people capture, create and share 360-degree content. We also revolutionized how we store high-resolution media, using deep neural nets to automatically reorient 360 photos.

Transitioning entirely to neural machine translation

Language translation is one of the ways we can give people the power to build community and bring the world closer together. It can help people connect with family members who live overseas, or better understand the perspective of someone who speaks a different language. We use machine translation to translate text in posts and comments automatically, in order to break language barriers and allow people around the world to communicate with each other.

Mobile SLAM at Facebook

The ability to place and lock in digital objects relative to real-world objects is known as simultaneous localization and mapping (SLAM), and it’s an ongoing challenge in computer vision and robotics research. Applied Machine Learning (AML) team of facebook used initial work done at Oculus in their Computer Vision group to build and deploy SLAM while solving the need for device-tailored algorithms, small-as-possible binary size, and a believable experience.

You can get more details about fb and Ml here

Machine learning is the most important technology for the business of the future. That’s because AI-driven software is already helping companies increase efficiency, improve customer relationships, and boost sales.

Some Myths related to ML/AI and jobs🙂

🤔Artificial intelligence is becoming good at many “human” jobs — diagnosing disease, translating languages, providing customer service — and it’s improving fast. This is raising reasonable fears that AI will ultimately replace human workers throughout the economy. 🤔

But that’s not the inevitable, or even most likely, outcome. Never before have digital tools been so responsive to us, nor we to our tools. While AI will radically alter how work gets done and who does it, the technology’s larger impact will be in complementing and augmenting human capabilities, not replacing them.

🎇Certainly, many companies have used AI to automate processes, but those that deploy it mainly to displace employees will see only short-term productivity gains. In our research involving 1,500 companies, we found that firms achieve the most significant performance improvements when humans and machines work together. Through such collaborative intelligence, humans and AI actively enhance each other’s complementary strengths: the leadership, teamwork, creativity, and social skills of the former, and the speed, scalability, and quantitative capabilities of the latter. What comes naturally to people (making a joke, for example) can be tricky for machines, and what’s straightforward for machines (analyzing gigabytes of data) remains virtually impossible for humans. Business requires both kinds of capabilities.🎇

“Job profiles would change, not the number of jobs but yes we have to be get upgraded with the new new techs coming in the market”

for eg- when computers came, then it was thought that all human beings would be replaced by computers especially in banks and commerce side BUT reverse happened. computers gave job to millions of people.

Computers were having capability to change world and hence people started learning it. Now in this era, technologies change the world so, we have to learn them.😊