Machine Learning In Daily Life!

 Machine Learning Application in Daily Life:






1. Commute Estimation

 

In general, a single trip takes more than average time to complete, multiple modes of transportation are used for a trip including traffic timing to reach the destination. Reducing commute time is not simple yet, here below you find how machine learning is aiding in reducing commute time,

 

  1. Google’s Map: Using the location data from smartphones, Google Maps can inspect the agility of shifting traffic at any time, moreover map can organize user-reported traffic like construction, traffic, and accidents. By accessing relevant data and appropriate fed algorithms, Google Maps can reduce commuting time by indicating the fastest route.

 

  1. Riding Apps: From how to fix the price of the ride, and how to minimize the waiting time to how do riding cars fix up one’s trip with other passengers to lessen diversion. Yes, the solution is machine learning.  ML assists the company to estimate the price of a ride, computing optimal pickup location and ensuring the shortest route of the trip, also for fraud detection. For example, Uber uses machine learning to optimizes its services.

 

  1. Commercial flights to use Autopilot:  With the help of AI technology, Autopilots are taken care of Flights now. In a report of The NewYork Times, pilots reported doing manual flying of seven minutes, mainly during takeoff and landing, and the rest fly is done by autopilot.

    2. Email Intelligence

     

    1. Spam Filters: Some rules-based filters aren’t served actively in an email inbox such as when, for example, a message comes with the words “online consultancy”, “ online pharmacy”, or from “unknown address”. 

     

    ML is offering a powerful feature that filters email from a variety of signals, like words in the message, metadata of the message(such as who sent the message, from where it is sent). Even though it filters the emails based on “everyday deals” or “welcome messages” etc. With the use of ML, Gmail filters 99.9% of spam messages. 

     

    1. Email Classification: Gmail categories emails into groups Primary, Promotions, Social, and Update and label the email as important.

     

    1. Smart Replies: You must have observed how Gmail prompts simple phrases to respond to emails like “Thank You”, “Alright”, “Yes, I’m interested”. These responses are customized per email when ML and AI understand, estimate, and reflect on how one counters over time.   

3. Banking and Personal Finance

 

  1. Fraud Prevention: In most of the cases, daily based transaction data is so high in volume and becomes complex for humans to review manually each transaction, then how to find out if a transaction is fraudulent.

 

To tackle this problem, AI-based systems are designed that learn what type of transactions are fraudulent. This is how banks use AI.

 

Companies are using neural networks to determine fraudulent transactions depending upon factors like the latest frequency of transactions, transaction size and type of retailer included.  

 

  1. Credit Decisions: When applying for credit cards or loans, the financial bodies have to determine quickly whether to admit or not. And, if accepting the proposal what could be the specific conditions to offer in terms of interest rate, credit line amount, etc.

 

Financial institutions deploy ML algorithms to make credit decisions and determine the particular risk assessment for users separately. 





4. Evaluation and Assessment

 

  1. In checking Plagiarism: ML can be used to build a plagiarism detector.  Many schools and universities demand plagiarism checkers analyze the writing skills of students.

 

The algorithmic essence of plagiarism is the similarity functions that result in the numerical estimation of how identical two documents are. 

 

  1. Robo-readers: Prior, essay grading is a very complex task, but now researchers and organizations are building essay-grading AI systems. The GRE exam grades essays through one human reader and one Robo-reader, known as e-Rater.

 

If the grade varies considerably, a second human reader is considered to settle the difference. (You may go to the article to know how Robo-readers functions).

 

Near in the future, one-size-fits classes are replaced by personalized and flexible learning that will shape each students’ strengths and weaknesses individually.

 

ML also assists in identifying students at-risk earlier so that schools can pay attention to those students by providing them with extra resources of learning and reduces dropout rates.

 

For example, AI in the education sector helps in for personalized learning, voice assistants, aiding educators in administrative tasks and many more.

For more information you can watch the given video:






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