What is Machine Learning? Machine Learning is the study of computer algorithms that improve their performances automatically through experience and have the ability to learn from data without explicitly programming them to do so. Humans are intelligent beings because ever since the advent of mankind, we have been striving to make our lives better and easier. This motivation of human beings has led us to three industrial revolutions that changed the course of history. According to many experts, we are fast approaching a fourth revolution which will be brought on by Machine Learning. Machine Learning now touches every aspect of our lives. Before the advancement of technology, the wonders of ML were limited to basic predictions, but now ML has the power to increase efficiency in any field, reduce costs, and make our lives easier. The applications of Machine Learning are everywhere. So, let us look at some ways in which ML is used in our day-to-day life.
Virtual Personal Assistants
The most celebrated application of Artificial Intelligence and Machine Learning that touches many personal lives is a virtual personal assistant. Alexa, Siri, Google Now, etc. are some of the popular examples of virtual personal assistants. Very similar to a human personal assistant, but much faster and more efficient, virtual personal assistants help you in finding information, when asked over voice commands. All you need to do is activate them and ask “When is my dentist appointment?”, “What are the flights from India to London”, or similar questions. To find the answers to your questions, your personal assistant looks out for the information on search engines, recalls your past queries, or sends a command to other apps to collect information. You can even ask the assistants to do certain tasks like “Set an alarm for 5 a.m. next morning”, “Remind me to pay the water bills today”. How does this work? Machine learning plays an important part in these personal assistants as they collect and process the information on the basis of your previous interactions with them. Later, the patterns from this data are utilized to deliver results that are adjusted according to your preferences. Other applications of ML in virtual assistance are Smart Speakers like Amazon Echo and Google Home, Smartphones like Samsung Bixby on Samsung S8, and Mobile Apps like Google Allo.
Traffic Predictions
Today, every vehicle and every mobile phone comes up with a pre-programmed GPS system. We all have been using GPS navigation services for our commutes to avoid traffic or to have a general idea of the time required. How is ML used in this? While we use GPS to search for various addresses, our current locations and velocities are being saved at a central server in order to manage traffic. This data is then used to build a map of current and future traffic. The challenge that such map builders face is that there are fewer cars that are equipped with GPS. Through this data, the algorithm does congestion analysis and that helps in preventing the traffic. Machine learning in such scenarios helps to estimate the regions where traffic can be found on the basis of daily experiences.
Email Spam and Malware Filtering
The first major application of Machine Learning was email spam filtering. Today, many businesses and email clients use a number of spam filtering approaches. To ensure that these spam filters are continuously updated, they are powered by machine learning. Why is ML used here? We can always use coding and programming to give certain instructions or keywords to the algorithm, but rule-based spam filtering sometimes fails to track the latest tricks adopted by spammers. Multi-Layer Perceptron, Decision Tree Induction are some of the spam filtering techniques that are powered by ML. Such algorithms detect over 325, 000 malware every day, and each piece of code is approximately 90–98% similar to its previous versions. Such dynamic pieces of code help us in detecting harmful emails and other malware from causing damage to our privacy and information.
Videos Surveillance
Video surveillance has helped law enforcement to work more efficiently and for common people to better protect themselves. There are millions of cameras that work together at any given moment. Imagine a single person monitoring multiple video cameras! In this case, there will a lot of inefficiency and bias. This is why the use of Machine Learning has become the need of the hour. The video surveillance system nowadays is powered by ML that makes it possible to detect crime or accidents before they happen. They track unusual behavior of people like standing motionless for a long time, frequent stalking of a place, stumbling, or napping on benches, and even physical symptoms of medical emergencies, etc. The system can thus give an alert to authorities concerned, which can ultimately help to avoid disasters.
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