Machine learning, the driving force of Artificial Intelligence, is a process where a computer system is taught to observe and analyze a massive amount of automatically extracted data to make predictions using its perception. What could have taken centuries is being analyzed within minutes or seconds with the help of machine learning. In the event of a prediction being made incorrect, machine learning learns to make a more correct prediction with respect to user views, searches, or online purchases. ML is constantly improving and shaping the future of IoT and AI.
A few examples of machine learning have been listed down that are being used in our everyday life and perhaps we have no idea that they are being driven by ML.
Some of the most popular examples of virtual personal assistants are Google Now, Alexa and Siri. All of these help us in finding information, which has been asked over voice. We need to activate them to ask queries. For providing answers to these, the selected and activated personal assistant searches the required information bears in mind the queries related to it or sends an instruction to other resources (phone apps for example) to collect the required information.
ML or Machine learning is a vital element of these personal assistants as they gather and filter the information based on our previous involvement with them. Afterward, this set of data is used to deliver results that are customized as per your preferences.
A variety of platforms that integrate Virtual Assistants are as follows:
Traffic Predictions: GPS navigation services are being used by nearly every commuter on road. In the course of doing that, our present locations and paces are being accumulated at a central server for managing traffic. These data are then used to put up a map of current traffic. Although this helps in averting the traffic and carries out an analysis of congestion, the fundamental problem is that less number of cars is equipped with GPS. In such scenarios, machine learning helps to approximately locate the regions where congestion is probably based on daily experiences.
Transportation Networks: Machine learning helps a cab booking app to minimize route diversions and accordingly estimate the price of the ride thus playing a major role.
Well, it is indeed a tedious and boring job for a single person to monitor multiple video cameras at the same time. As an aid to this issue, today’s video surveillance system is powered by AI as a result of which it is possible to detect crime before it happens. The unusual behavior of people such as standing without any motion for long hours, napping on benches, etc. are tracked with this technology. As soon as such things happen, the system triggers an alert to human attendants on duty, ultimately helping to avoid mishaps. And with the report, count, and correctness of such activities, the surveillance services can be improved a lot. And this is possible only when machine learning is executing its job at the backend.
Social media platforms are using machine learning (ML) for the benefit of users like personalizing news feed for better targeting of ads and many other commonly noticed features that are being used without the smallest notion that these wonderful features are the applications of machine learning. Some of these features are:
“People You May Know”: In Facebook, a user always sees this feature “People you may know”. Machine learning technology helps the Facebook application keep a track of the friends that are being connected with, the profiles that are visited very often, the special interests, the workplace of the user, or a group that is shared, etc. Based on continuous learning, Facebook suggests “People you may know” to its users to become friends with.
Face Recognition: Once a picture is uploaded by a user with a friend, Facebook immediately recognizes that friend. The projections and the unique features of the photo are observed by Facebook, and then the findings are matched with the people in the user’s friend list. Well, once again it is a simple application of Machine Learning.
Similar Pins: Being the core factor of Computer Vision, machine learning uses a technique to pull out useful information from videos and images. Pinterest uses computer vision to identify the objects in the images (or pins as is called in this application) and accordingly recommend similar pins.
Email clients use a number of spam filtering approaches. To make certain that these spam filters are constantly updated, they are powered by ML or machine learning. Rule-based spam filtering is unable to follow the newest tricks adopted by spammers. But today ML powered spam filtering techniques like Multi-Layer Perceptron, or C 4.5 Decision Tree Induction can track the same.
Nowadays websites offer the user the option to chat with their customer support representative while navigating within the site. Unfortunately, a live executive to answer our queries are not available with a lot of websites. In most cases, a chatbot service is offered to answer our queries where the bots have a tendency to dig up information from the website and offer it to the customers. With the passage of time, the chatbots have also advanced as they are now in a position to understand the user queries better and provide them with better answers, all due to its machine learning algorithms.
Machine learning is used by Google and other search engines to improve the user’s search results. Whenever a search is executed, the algorithms at the backend tracks our way of responding to the results. If a user opens up the top results and remains on the web page for a long time, it is being assumed by the search engine that the displayed results were in accordance with the query. Similarly, on just scrolling through the second or third page of the search results without opening any of the links, the search engine arrives at the conclusion that the served results did not match requirement. Machine learning algorithms work like this constantly at the backend to improve our search results.
If an online product has been purchased a few days back, we will notice that we are constantly receiving shopping suggestion emails or showed some recommendations that somehow match our taste. This refined shopping experience has been possible because of machine learning. Based on our behavior with a particular website or an app, purchases made earlier, brand preferences, items liked or added to cart, etc., the product recommendations are made by ML.
Tracking of monetary frauds online and to make the cyberspace fully secured, Machine learning has endeavored to prove its potential to its fullest. For example, ML is being used by Paypal for safeguard against money laundering. A set of tools are used by the company that assists them to compare millions of transactions taking place and differentiate between illegitimate or legitimate transactions taking place between the sellers and the buyers.