Companies using Machine Learning use cases in Cool Ways for their profit : Case Studies

Hello Guys

I hope you all are doing well .I’m here once again with a new blog on the case studies of Machine Learning and Artificial Intelligence. How Machine Learning is used by big MNCs for their profit.

Let’s start

What is Artificial Intelligence?

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and observe their actions. The term may also be applied to any machine that inherits traits associated with a human mind such as learning and problem-solving.

AI is huge concept which includes machine learning , deep learning, natural language processing and all.

What is Machine Learning?

Machine learning (ML)is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves .i.e. how we make machines intelligent.

Difference between ML and AI?

Artificial Intelligence is the broader concept of machines being “smart”. Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves. Although ML is a subset of AI. Both are different.

Applications of Machine Learning

The value of machine learning technology has been recognized by companies across several industries that deal with huge volumes of data. By leveraging insights obtained from this data, companies are able work in an efficient manner to control costs as well as get an edge over their competitors. This is how some sectors / domains are implementing machine learning -

Financial Services : Companies in the financial sector are able to identify key insights in financial data as well as prevent any occurrences of financial fraud, with the help of machine learning technology. The technology is also used to identify opportunities for investments and trade. Usage of cyber surveillance helps in identifying those individuals or institutions which are prone to financial risk, and take necessary actions in time to prevent fraud.

Marketing and Sales : Companies are using machine learning technology to analyze the purchase history of their customers and make personalized product recommendations for their next purchase. This ability to capture, analyze, and use customer data to provide a personalized shopping experience is the future of sales and marketing.

Government : Government agencies like utilities and public safety have a specific need FOR ML, as they have multiple data sources, which can be mined for identifying useful patterns and insights. For example sensor data can be analyzed to identify ways to minimize costs and increase efficiency. Furthermore, ML can also be used to minimize identity thefts and detect fraud.

Healthcare : With the advent of wearable sensors and devices that use data to access health of a patient in real time, ML is becoming a fast-growing trend in healthcare. Sensors in wearable provide real-time patient information, such as overall health condition, heartbeat, blood pressure and other vital parameters. Doctors and medical experts can use this information to analyze the health condition of an individual, draw a pattern from the patient history, and predict the occurrence of any ailments in the future. The technology also empowers medical experts to analyze data to identify trends that facilitate better diagnoses and treatment.

Transportation : Based on the travel history and pattern of traveling across various routes, machine learning can help transportation companies predict potential problems that could arise on certain routes, and accordingly advise their customers to opt for a different route. Transportation firms and delivery organizations are increasingly using machine learning technology to carry out data analysis and data modeling to make informed decisions and help their customers make smart decisions when they travel.

Machine Learning Applications and Use Cases in Top Companies

1. Pinterest

Pinterest is a treasure chest of data that the company is constantly combing through to provide the best tailored experience for the user through pin recommendations. With the help of machine learning, Pinterest identifies content that resembles previous users pins and recommends that content to the user. The algorithms help provide inspiration to the user that he or she may have never initially searched or pinned.

Uses Pinnability: Machine learning in the home feed

Pinnability is the collective name of the machine learning models we developed to help Pinners find the best content in their home feed. It’s part of the technology powered by smart feed, which we introduced last August, and estimates the relevance score of how likely a Pinner will interact with a Pin. With accurate predictions, we prioritize those Pins with high relevance scores and show them at the top of home feed.

2. Facebook

Facebook is actually not even a social network but a global phenomenon. And obviously, Machine Learning is a 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! Facebook uses Machine Learning in the working of all its aspects with plans on enhancing it even further.

Friend suggestions section: Facebook uses Machine learning to help you with some friends suggestions to add them to your profile or friends list. This is done through either the “Suggested Friends” section or “People You May Know” section as well.

News Feed: The more you visit a friend’s profile on Facebook, or your close friends, or even the pages that you visit on Facebook, the more their news or posts appear at the top of your feed. This is also because of ML.

Mutual Friends list: Even if you check a new profile of a new friend, Facebook will prompt you with the mutual friends list shown on the screen so you could better make your decision of adding someone unknown to your friends’ list.

Relevant Advertisements on your Feed: You may also notice a few ads that might come up on your feed relevant to the past searches you must have made on the Internet.

Keeping away abusive and obscene content away: The security features of Facebook also use ML to help you protect from the abusive and obscene content and even unknown users.

Translator services: If at all any person posts in a language not known to you, automatically Facebook gives you the translator functionality at the bottom of the post to help you understand the language by translating it into English.

Automatic tagging: Suppose if you post a photo with your friends, Facebook will automatically suggest you tag if you want to tag your friends in the same. This is also a feature of the ML algorithms.

3. Netflix

As the world’s leading Internet television network with over 160 million members in over 190 countries, our members enjoy hundreds of millions of hours of content per day, including original series, documentaries and feature films. They invest heavily in machine learning to continually improve their member experience and optimize the Netflix service end-to-end. As researchers, they innovate using machine learning in many areas where we prototype, design, implement, evaluate, and productionize models and algorithms through both offline experiments and online A/B testing.

Machine learning impacts many exciting areas throughout the company. Historically, personalization has been the most well-known area, where machine learning powers our recommendation algorithms. They’re also using machine learning to help shape their catalog of movies and TV shows by learning characteristics that make content successful. They use it to optimize the production of original movies and TV shows in Netflix’s rapidly growing studio. Machine learning also enables us to optimize video and audio encoding, adaptive bitrate selection, and in-house Content Delivery Network that accounts for more than a third of North American internet traffic. It also powers their advertising spend, channel mix, and advertising creative so that they can find new members who will enjoy Netflix.

I’ve listed few but there are more top and normal companies using Machine Learning for their benefit.

Not only companies use machine learning but in daily life too we all uses machine learnings

Machine Learning Applications and Use Cases in our Daily Life

1. Machine Learning Use Cases in Smartphones

Do you know that your phone has multiple features of Machine Learnings in it. Some of them are listed below

Voice Assistants

That example we saw in the introduction about talking to our virtual assistant? That was all about the Concept of Speech Recognition — a budding topic in machine learning right now.

Different companies voice assistants

popular voice assistants:

Apple’s Siri

Google Assistant

Amazon’s Alexa

Google Duplex

Microsoft’s Cortana

Samsung’s Bixby

Smartphone Cameras

Nowadays Cameras also uses machine learning algorithms for better performances. They analyze every pixel in a given image to detect objects, blur the background, and lots of more things.

Face Detection on AI based Camera

Face Unlock

Our smartphone unlocks itself by detecting our face. It’s smart, efficient and time-saving.

2. Machine Learning Use Cases in Transportation

Google Maps

Google Maps is a prime example of a machine learning use case. When you open Google Maps you’ll see options like

->Routes: Go from point A to point B

->Estimated time to travel this route

->Traffic along the route

->The ‘Explore Nearby’ feature: Restaurants, petrol pumps, ATMs, Hotels, Shopping Centers, etc. And it works almost perfectly.

3. Machine Learning Use Cases in Popular Web Services

Email filtering

Email also uses some Machine Learning Algorithm. It allows us to customize labels, the service offers default labels:

->Primary

->Social

->Promotions

The machine learning algorithms immediately categorize the email into one of these three labels as soon as you receive an email. We get an instant alert if Gmail deems it a ‘Primary’ email. Gmail also uses machine learning to figure out if the email is spam or not.

Email filtering spam mails

It can also be used in various fields like translations, recommended ads on Facebook and Instagram, chat-bots, security cameras and many more. It all uses machine learning concepts and its algorithms. Machine Learning has vast field.

Popular Machine Learning Use Cases

Self driving cars

Out of all the use cases we have covered in this article, self-driving cars fascinate me the most. It is a crowning achievement of what we have been able to accomplish using hardware and machine learning.

The beauty of self-driving cars is that all the three main aspects of machine learning — supervised, unsupervised and reinforcement learning — are used throughout the car’s design.

This is How driverless car sees the world

Here are just a few features of self-driving cars where machine learning is used:

->Detecting objects around the car

->Detecting the distance between the car in front, where the pavement is located, and the traffic signal

->Evaluating the condition of the driver

->Scene classification, among many other things.

Machine Learning has endless opportunities for future. We are lucky living in this era.

Thank you guys for reading this !!

Hope you got some knowledge !!

With this I end my blog now. Further I’ll come with new more blogs.

Resources:

https://www.quora.com/How-does-Facebook-use-machine-learning-1

https://labs.pinterest.com/projects/machine-learning/

https://research.netflix.com/research-area/machine-learning#:~:text=We%20use%20it%20to%20optimize,of%20North%20American%20internet%20traffic.

Pictures/gifs: Google

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Computer Science Major

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Sanchita Agrawal

Sanchita Agrawal

Computer Science Major

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