23 Mar 5 Ways Machine Learning Impact On Our Daily Life
Machine Learning, a subset of Artificial Intelligence, is gaining immense momentum and popularity in recent years. In basic words, these are intelligent machines that act on behalf of humans. Conceptually, it trains the machines to study the patterns in data and use them in specific problems. On the basis of predictions and previous computations, they offer repeatable and reliable results that require minimal human intervention.
Machine learning aids not only the professional and industrial procedures but also is very impactful in the day-to-day lives. There are various touch-points in everyday life where this action intelligence has enhanced the working. Voice-controlled personal assistants are the most common examples of Machine learning in routine life. Apart from that, there are smart homes building up, driverless cars, and the list goes on.
Below are the most common five ways in which Machine learning has induced into our lives.
Topics Covered
1.TRANSPORTATION
Nowadays, traveling through cabs is not a troublesome affair as there are ample options available like Ola, Uber, etc in almost every part of the world. These apps are the perfect example for making the most of Machine learning. The ML algorithms in the app help in finding out the route, getting the most immediate driver present, also checking out the fastest route available, price fare, kind of cab, and much more.
Moreover, it even tells you about any disturbance in the route due to traffic, construction, jam, etc, and gets you the alternative route. Also, you get full details of the driver and his cab. Even you can share your live location with your family and friends to check out your status.
Other popular illustrations of ML in transportation are image detection to automate self-driving cars, determining engine safety by collecting data to predict engine’s health, traffic management by traffic sign detection, etc.
2.DETECTING FRAUD IN TRANSACTION
There are an innumerable amount of transactions taking place in a single day. And the probability of getting the fraudulent ones in between also rises. It is practically impossible for humans to manually keep a watch on each action and find out the suspicious one. This will cause huge financial losses to both banks and the potential customers. The expected revenue generation may be hampered.
ML algorithms act as the savior here. It not only reduces human efforts but also chances of fraud. By using the location data of the client and the device being used for transactions ML detects the fraud being made and immediately alerts the client with a warning message or email.
ML is the heart of all online businesses.
3.ONLINE VIDEO STREAMING (NETFLIX)
Online video streaming is the new most promising trend to watch out the web series and movies, especially after the pandemic when multiplex cinemas were temporarily shut down. There are many budding platforms in this direction namely, Netflix, Amazon Prime Video, Hotstar, SonyLiv, etc.
However, Netflix definitely rules the online streaming world with more than 100 million subscribers. Machine learning is the answer to all the success of Netflix. That is why the popularity of Netflix skyrocketed in Hollywood and the Blockbuster failed.
The Netflix algorithm keeps a track of their customer data like ratings & searches, pause, rewind, fast forward done, date & time of watching, scrolling behavior, which movies/shows are watched, etc.
By using this data for each of their subscribers, they apply a recommender system with the help of a lot of ML applications. This way, they succeed in retaining a lot of their clients.
4.SEARCH ENGINES
It is only the power of Machine learning that search engines like Google, Bing, Yahoo, etc. are everyone’s first resort to any sort of query. A plethora of relatable links is displayed after the user types out any query and press enter. It seems that the search engine already knows what we are looking for.
This is because the search engines apply algorithms like Deep Learning, Tensorflow, and Natural Language Processing (NLP) to decode the meaning of search queries. Further, it ensures that the high quality most relevant results are shown at the top of the SERP. Google uses the machine learning model named RankBrain to predict searches and find a relation between users and searchers.
5.DETECTING SPAM EMAILS
Emails are used by every person availing of online services, nowadays. Whenever an email id is logged In, a ‘spam’ folder always appears in the sidebar. This folder generally has all the unwanted emails. But these emails are not manually transferred to the spam folder, rather it is done automatically by ML.
For spam-filtering, various algorithms are used including, K-Nearest Neighbours, Random Forest, and Naive Bayes, etc among the popular ones. They use the mails that have already been classified as spam or non-spam as the training set.
This training set is applied to the new emails to classify them in these two folders. ML algorithms are capable to detect even fraud emails.
CONCLUSION
ML is the fastest growing industry and has a promising future ahead. Due to its vast applications in almost all sectors, it is influencing our daily lives like never before and making it more convenient.
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