HOW MNCs USE ML/AI AT ITS BEST?
What is Machine Learning?
Machine learning is a subset of artificial intelligence (AI) that seeks to analyze patterns in data to advance decision-making and learning. Though we might not notice machine learning, it’s everywhere in our lives. When we browse sites like Netflix or YouTube, we are recommended certain videos and shows to watch. That’s machine learning! Companies like Amazon or Google are also creating smart speakers that utilize machine learning, e.g., Siri, to become our personal assistants.
Why is Machine Learning important?
People and devices continuously generate data, and they aren’t stopping anytime soon. Data is the life of all businesses because it allows us to automate tasks and make data-driven decisions, which, in turn, eases our everyday lives.
Before, people had to analyze data to make decisions manually. However, as the amount of data in this world expands, it’s increasingly important to utilize AI and machine learning to capture data fully.
How is machine learning used?
Machine learning is very literally everywhere. It’s wherever data is being used and analyzed. For example, Google utilizes machine learning for its GPS navigation services or its search engine. Another application is image detection or fraud detection, which is often used by many financial holdings companies.
The use of machine learning has also exploded in the health industry. It is being used to detect health problems and make quick diagnoses. For example, health professionals use data on past skin cancer patients to develop a model that helps identify current patients with skin cancer. The potential for machine learning is boundless.
Machine Learning has helped big MNCs a lot since the last few decades. Nowadays Machine Learning is growing at its peak and competition level in this field is increasing day by day.
Companies are using ML in various cool ways :
How Netflix is using ML?
Netflix is a streaming service that offers a wide variety of award-winning TV shows, movies, anime, documentaries, and more — on thousands of internet-connected devices.
Netflix has a huge collection of content and day by day it is increasing rapidly so users might not able to find relevant content of their interest. That’s why Netflix uses a recommendation system to recommend movies and shows to its users. This is one of the best features of Netflix. Netflix uses what history of its users to recommend which shows and movies the user would be interested in watching. It allows users to consume data in the best way.
How Twitter is using Machine Learning?
Twitter is a social networking platform that allows its users to send and read micro-blogs of up to 280-characters known as “tweets” with their followers. It is important for news reporting, event promotion, marketing, and business. Twitter uses artificial intelligence to improve user experience.
Twitter uses artificial intelligence to recommend relevant tweets to its users. Twitter’s artificial intelligence algorithm scans thousands of tweets per second and ranks them for every user’s feed. Twitter also uses AI to filter inappropriate content from the platform and in ranking the tweets. Twitter’s ranking algorithm has lots of data that it has processed through deep learning model and has learned what would be the relevant tweets for any particular user. All the tweets are scored based on the ranking mode whether the user would like it or not. Twitter uses IBM Watson and NLP skills to track and remove abusive tweets.
How Tesla is using Machine Learning?
As we all know Tesla is the pioneer in the field of manufacturing electric cars. The goal of Tesla was to prove that electric cars could be better over traditional fuel-powered cars.
Tesla uses ML in the cloud which is responsible to educate the entire group. They use some edge cutting modules in it which decides what action needs to be taken. The cars are also able to form networks with other Tesla vehicles nearby to share some information. Tesla has used existing customer databases for its data analytics. This data is used to understand customer requirements and regularly updating the requirements accordingly.