We all know that Machine Learning in the form of AI technology is continuing to gain popularity in our lives. Machine Learning(ML) is a technology that involves a group of algorithms that allow software systems to become more accurate and precise in predicting outcomes without being programmed explicitly. In other words, in ML, algorithms receive input data and use statistical analysis to predict the outcome, thus giving the ability to the computer to think like humans. There are a lot of day-to-day scenarios that involve the use of ML in our lives; perhaps we do not pay attention to it.
Let us now see how we come across Machine Learning in our real-life.
Real-World Machine Learning Applications
1. Healthcare and Medical Diagnosis
Machine Learning involves a variety of tools and techniques that helps solve diagnostic and prognostic problems in a variety of medical domains. Prediction of disease progression, for extraction of medical knowledge for outcomes research, for therapy and planning and support, and overall patient management are some examples where we use machine learning for the analysis of clinical parameters. We also use ML for the data analysis of medical records such as detecting regularities in data, dealing with incomplete data interpreting continuous data produced by the Intensive Care Unit, and also for intelligent alarming resulting in efficient and effective monitoring.
2. Commute Predictions
- Predicting Traffic: We all use GPS services to navigate while driving. ML, in such scenarios, helps us in our daily lives to avoid traffic and reach our destination on time. Programmed GPS works such that while we use it to navigate, it saves our locations and velocities in the central server of managing traffic, which is then used to build the map of the current traffic. ML thus does a congestion analysis, the only downside is that it sometimes seems inaccurate if lesser number of cars use GPS while driving.
- Online Transport Applications: We have all used cab booking applications like Uber, Ola, and Lyft; all such applications predict the price and ETA of the trip at the time of booking itself. ML algorithms define the mechanism behind such applications.
3. Social Media
Most of us are addicted to social media these days and why wouldn’t we be? Social media is fun and engaging, from teaching you DIYs and new stuff through videos to news and networking. ML technology plays a crucial role in developing user-friendly social media websites and applications.
- Suggesting Friends: Social networking sites like Facebook keep track of the friends that we connect with, profiles we visit frequently, shared groups, interests, and workplace. Based on continuous learning, Facebook suggests people with whom we can be friends.
- Face Recognition: Social websites and applications like Facebook and Instagram immediately recognize our friends the moment we upload the picture on media and start giving notifications to tag them. Although the interface is quite user-friendly and looks comfortable at the front end, the entire process at the backend is quite complicated.
4. Smart Assistants
Using smart assistants is what we do round the clock. We all have used Siri, Google Assistants, Alexa, and many more on our smartphones like Pixel and iPhone and smart speakers like Echo and Google Home. In addition to this, Samsung is also in the run of launching a smart TV with its virtual assistant called Bixby. As “assistants,” their job is to assist us in our day-to-day routine, all that is required from our end is to activate them.
The significant use of assistants in our everyday lives involves activities like setting alarms and reminders, updating the user with live news through notifications. Answering questions like “What are the price of Hotels in Japan?” or “Any Italian Restaurant near me?” is also handled by these smart assistants. These assistants can look for information, recall-related queries, or send a command to other resources (on the web) to collect info and answer the user’s questions.
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5. Optimizing Search Engine Results
Search engines like Google make use of machine learning algorithms to improve search results. Algorithms keep track of our response to the results shown to us. For example, if the results generated are efficient and useful to the user, the user would stay on the webpage for a long time, and this would help search engines learn that the results generated are in accordance with the query. On the opposite, if the results are not useful and the user moves to the 4th or 5th page of the search results without opening any webpage in between, the search algorithm would notice that the results were not efficient and did not serve the purpose.
6. Video Surveillance and Security
It is tedious as well as annoying to keep track of hundreds of surveillance cameras by a single individual or even a small team of security. ML trained surveillance cameras to make this task easier by detecting any crime before it happens. The cameras are programmed to keep a close eye on the general public and notice suspicious activities, if any, for example, if anyone is standing still for an extended period, or someone is visiting a spot quite often to inspect it. The smart cameras notify the human attendants if they predict any mishap, thus saving the lives of many.
7. Cyber Security
Machine Learning offers a potential insight in preventing online monetary frauds, thereby making cyberspace a secure place for transactions and net banking. Applications like PayPal, GPay, Paytm have a set of tools that help them keep track of transactions and distinguish between legitimate and illegitimate transactions, thus preventing any false transactions.
8. Customer Service
Ever seen a chat box popping up when you visit certain websites? There is a good chance it is an ML programmed chatbot. They play the role of the customer care representative to help the user with their queries. The bots are programmed to answer the user by extracting information from the site’s data store.
ML algorithms enhance the capability of the bots to advance with time by understanding the user queries and serve them with the right answers.
9. Email Spam
Several spam filtering approaches are used these days by email clients and other applications. To ensure the security and that these spam filters are continuously updated, they are powered by ML algorithms. The latest trick of spammers can easily be detected by observing specific patterns and by rule-based spam filtering. Examples of some spam filtering techniques are Perceptron and C 4.5 Decision Tree.
10. Product Recommendation
There is no doubt to the fact that online shopping has taken over the retail market in the past few years. Online shopping provides a great experience with a variety of options for a given product, competitive discounts and also comes with the facility of home delivery. Nowadays, you might have noticed that if the user searches or purchases a product from a website or an application, similar or same products are recommended to the user on their next visit to the application. Product recommendations are made on the basis of the behavior of the website or application, past purchases, items liked or wishlist, and finally, items that were bought. This refined shopping experience is because of ML running at the backend of the application or websites.
Where do you use Machine Learning in your daily life?
Apart from the applications stated above, there are substantial other sectors and areas which implement ML technologies. Do share how machine learning is changing your life and making your life more comfortable in the comments below.
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