Machine Learning Applications

Artificial Intelligence

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27 Apr 2022
6 Min read

Top Uses Of Machine Learning Applications In Day To Day Life

Artificial Intelligence (AI) is becoming increasingly prevalent. You may already use it somehow and aren’t even aware of it. Machine Learning (ML), in which computers, software, and gadgets perform via cognition, is one of the most common applications of AI.

The application of Machine Learning in numerous disciplines is growing in popularity as the amount of data available grows. Machine learning presents many ways to extract information from data and turn it into actionable goals.

ML algorithms can supplement field data and automate functions that are mostly linked to regulation and optimization. Furthermore, machine learning and computer vision have enhanced many disciplines, including medical diagnostics, statistical data analysis, methods, scientific research, etc. Such techniques have already been used in smartphone applications, computer appliances, online websites, cybersecurity, etc.

Obtaining interferences and essential knowledge from data have emerged as the current model of scientific investigation and commercial application today, spanning numerous fields. This blog will look at several machine learning applications we’ve seen in our everyday lives.

Here, we provide a few examples of machine learning that we use daily but may not realize are powered by ML.

Applications Of Machine Learning In Everyday Life

Estimation Of Travel Time

A single trip takes longer than usual since it involves many modes of transportation and traffic timing to reach the destination. Reducing commuting time isn’t accessible yet; learn how machine learning can help.

  • Google Maps

Google Maps can check the agility of shifting traffic using location data from cellphones, and it can also categorize user-reported traffic such as construction, traffic, and accidents. Google Maps can help you save time on your commute by offering the quickest route based on relevant facts and properly fed algorithms.

  • Riding Apps

From how to determine the cost of a journey and how to reduce waiting time to arrange one’s trip with other passengers to reduce diversion. Machine learning is, indeed, the answer. Uber, for example, enhances its services using machine intelligence. The startup uses machine learning to assess the cost of a ride, compute the best pickup location, ensure the quickest route possible, and detect fraud.

  • Commercial Flights To Use Autopilot

Autopilots are now in charge of flights, thanks to AI technology. According to The New York Times, pilots reported doing seven minutes of manual flying, mostly during takeoff and landing, while the rest of the flight is done by autopilot.

Intelligence On Email

  • Spam Filters

When a message contains the terms “online consultancy,” “online pharmacy,” or “unknown address,” for example, some rules-based filters aren’t served actively in an email inbox.

ML has a sophisticated function that filters email based on various signals, such as words in the message and message information. Even though it filters emails based on “daily discounts” or “welcome messages,” for example. Gmail filters 99.9% of spam messages using machine learning.

  • Email Classification

Gmail divides emails into Primary, Promotions, Social, and Update categories and labels them as necessary.

  • Smart Replies

You’ve probably noticed that Gmail encourages you to respond to emails with simple phrases like “Thank You,” “Alright,” and “Yes, I’m interested.” When ML and AI analyze, estimate, and reflect on how one counters over time, these responses are personalized in every email.

Personal Finance And Banking

  • Preventing Fraud

In most cases, daily transaction data is so large that it becomes difficult for humans to manually evaluate each transaction and then figure out if a transaction is fraudulent.

AI-based systems that learn whether fraudulent transactions are being developed to address this issue. Companies utilize neural networks to identify fraudulent transactions based on recent transaction frequency, size, and merchant type. This is how banks employ artificial intelligence.

  • Credit Decisions

When applying for credit cards or loans, financial institutions must make a swift decision on whether or not to accept the application. And, if the idea is born, what are the particular conditions to be offered in terms of interest rate, credit line amount, and so on? Financial firms utilize machine learning algorithms to make credit decisions and assess each user’s risk individually.

  • Check Deposit On Mobile

Furthermore, AI technology has made mobile banking personalized and convenient for individuals who do not have time to go to the bank. Banks, for example, allow users to submit checks via a smartphone app, eliminating the requirement for users to present a statement to the bank physically. Most banks utilize Mitek’s optical character recognition technology to translate and convert handwriting on checks into text.

Assessment And Evaluation

  • Plagiarism Detection: 

Machine learning can be used to construct plagiarism detection. Many schools and institutions require plagiarism checkers to evaluate students’ writing ability. The algorithmic essence of plagiarism is similarity functions, which produce a numerical estimate of how similar two papers are.

Conclusion

It’s beautiful how machine learning and artificial intelligence have made our lives easier. We may expect even more technical improvements in the future, thanks to some AI and ML developments. We’ve looked at various apps here, and machine learning is being utilized in the real world to touch our daily lives. For industries that wish to stand out in the market, it also allows us to make business decisions, optimize processes, and increase productivity. So, if you wish to know more about machine learning, contact the ONPASSIVE team.

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