Machine learning is everywhere, humans are using it extensively thereby generating more data, which in turn is helping the machine learn better. It is essential to know that Machine Learning is a part of the Artificial Intelligence environment.
Unknowingly humans are using many algorithms from Machine Learning in their daily lives. Many industries are leveraging the power of ML to achieve their goals, get deeper insights into their data, analyze them and take relevant decisions based on it.
Let us look at a few significant real-world applications of Machine Learning:
Ever wondered how Facebook gracefully suggests friends and helps in identifying faces? All thanks to image/face recognition algorithms that help in detecting faces and tagging them for mutual friends. Applications keep track of the people we connect with (online), groups that we are a part of, interests, and common friends. Based on the concept of continuous/active learning, applications suggest potential people with whom we would connect to.
Feeds of user accounts are personalized based on what they watch, what they shop, which helps provide relevant advertisements according to their past choices.
It involves extracting information from images and videos. Pinterest app uses computer vision to identify objects (commonly known as pins) and suggest similar pins to users. If two or more users have some number of common pins, they are suggested pins of each other by understanding that if user A has interest in a, b, m and n, user B has interest in a, n, x and z, user A might be interested in x and z, user B might be interested in b and m.
The most common application of ML is spam detection is emails. Every time a user sees an unwanted email in their account, they either ignore it or delete it. Machine Learning makes sure that such unwanted mails are not directed towards the inbox. They are, in contrast stored in a different folder, usually named 'spam'.
How is this done? Once a user marks a mail as 'spam', Machine Learning algorithms (integrated with email service providers) classify such mails as spam by looking at the number and type of words in the spam email, the combination of words and many such criteria. The next time one such email shows up, the ML classifier marks it as spam and stores it in the 'spam' folder.
Google Assistant, Amazon Alexa, Siri by Apple, all of these are considered as personal assistants, since they assist the user in performing certain task. They use voice recognition techniques to identify what the user is asking for. They can be initiated by activating them with a specific statement.
The information provided by the user is collected and refined, based on how the user previously involved in a conversation with the assistant. Based on this data, the responses are rendered to the user.
How does ML figure out the location of traffic congestion and show it in red colour on the map? Based on GPS equipped in cars and the daily used routes, ML helps detect traffic congestions.
When a user searches for a location, maps sometime provide suggestions and the user selects one of these suggestions or provides an altogether new location. When user selects a location from the recommendation provided, map learns about the user’s preference. Similarly, when the user selects a new location, it collects this data and stores it as the user’s preference.
Instead of human interference while dealing with trivial questions, chatbots (commonly known as ‘bots’) are used. They are programmed to talk in a human-like manner and are mostly available to answer the user’s query. They simply extract data from websites and give it to the customers.
There are different kinds of chatbots, some which rely on rules (rules defined that help in interacting with customers) and some that use reinforcement learning (learning based on feedback received). This way, human resources can be used for other tasks which require human assistance.
Cyberspace can be made a secure place by implementing ML algorithms that detect fraud, money laundering and other wrongdoings. Sometimes, when we buy products continuously and might pay using credit cards. Algorithms detect this as illegitimate transactions and send notification to the owner of the card enquiring if the transactions are theirs or not.
This could be movie recommendations, product recommendations on shopping applications, or just advertisements based on what the user previously searched for/shopped. For example: When a user shops for a baby product on an e-commerce website, the app will suggest other products which are related to the baby product or babies.
If users sign up for subscriptions, they also receive push notifications or emails about products “which might interest you” or “you will love this”. Based on what we watch, and what we buy, Machine Learning algorithms provide suggestions for better experiences.
In this post, we saw how Machine Learning has become a part of our day-today life and how we are using it extensively