Machine Learning In Daily Life Routine
Anything 'for the first time' will certainly have plenty of predictions made and discussions on how it would be taken over in the future. Earlier when the computers got invented or when the idea of ‘Machine Learning’ was born it was all the same through predictions and foresight. However, reality overtook all the fear and concern as the technology got even smarter than anticipated. Machine Learning in daily life is getting successfully applied in all aspects right from speech recognition apps in smartphones to YouTube recommendations and more.
We literally teach machines on what we like and wish through our smart devices on a daily basis. The more is our data, the smarter gets the algorithm. A perfect comment on ‘Machine Learning’ would be that "Humans cleverly tricking the very machines they originally created!" Now ML being as a daily reality, soon will not only replace manual labor but also mental labor. But how exactly will this happen? And is it already happening? So here, let's get to know the Popular Applications of Machine Learning in Daily Life and their impactful roles.
The Machines Know Us Better
No more halting to ask directions or a delay in planning trips, its probably because of the smart assistants in our hands. Reducing the travel time generally a trip takes is not simple yet, here below you find how machine learning in daily life aids reducing commute time.
Google Maps has certainly made it possible of getting down in any new place and marching just in the right direction depending on none. On being a machine learning app Google maps use the current location data from smartphones and inspect the agility of shifting traffic at any time. Moreover, it organizes user-reported traffic like construction, traffic, and accidents by accessing relevant data and appropriate fed algorithms.
Also, Google Maps reduces the commuting time by indicating the fastest route and also suggests stating: “Despite the Heavy Traffic, you are on the fastest route“. But, How does it do that?
Well, it is like when everyone using maps provide their exact location, average speed, and their travel route using GPS navigation services, that helps Google collect massive data about the traffic, and helps them predict the upcoming traffic and adjust our route accordingly. Also, It’s a combination of people currently using the service and historic data of that route collected over time.
Online Ride Apps:
You book a cab and the app estimates the price of the ride, You share these services, then they minimize the detours and How is all that? Well, in this plot its the machine learning that aids to do the fixing of ride price, minimizing the wait time, fixing up one's trip with other passengers while one another ride, and more. Machine Learning in daily life acts as a solution that assists the companies in estimating the price of a ride, computing optimal pickup location, and ensuring the shortest route of the trip, also for fraud detection.
On any individual applying for credit cards or loans, the financial bodies make in a quick decision on its approval with the help of ML. The Financial institutions do this with ease as they deploy ML algorithms to conduct a risk assessment for users separately and determine whether to admit the request or not based on the resulting factor. Also, ML is the one that predicts certain required conditions to offer like, interest rate, credit line amount, etc.
On top of all, the countless bank account holders, the number of credit cards that are in circulation, and the thousands of transactions are kept in track with the help of ML.
The increased volume of daily based transaction data makes the manual review to be complex and less secure. To overcome this problem, AI-based systems are designed and trained with the types of fraudulent may cause. Also, companies use neural networks to determine fraudulent transactions based on factors like the latest frequency of transactions, transaction size, and type of retailer included.
Some applications of machine learning,
It's ML in social media platforms, that draws you back and acts just the way you would want by personalizing your news feed and targeting better ads. The applications of ML in social media is what makes it more interesting and fun. Now, let's discuss a few of its features.
People you may know - Machine learning does this just by understanding from experiences. They keep track of your actions like the friends you connect with, often visited profiles, your interests, workplace, etc As a result of deep learning, it suggests you a list of Facebook users that you might like to make friends.
Auto-Tagging - Facebook uses AI and ML to identify faces and so on uploading a photo on Facebook it instantly recognizes faces and suggests friends tag. It uses the ANN algorithm that powers facial recognition software and makes perfect tagging possible.
The simple working of Pinterest, to automatically recognize objects in the images or “pin” and recommends similar pins happens by the computer vision it employs. The core element of Computer Vision is Machine learning that aids to extract useful information from images and videos.
Smart Personal Assistants
There are plenty of functions and use when it comes to Virtual Personal Assistants as they act just in a matter of a question you ask or text. At present, the personal assistants are being used in Chatbots, Online Training Websites, and also in Commuting Apps. They incorporate certain major applications of Machine Learning like,
Speech to Text Conversion
Natural Language Processing
Text to Speech Conversion
Eg: Siri, Cortana, Google Assistant, Amazon Alexa, Google Home, etc
Firstly, Machine Learning with a whole lot of tools and techniques helps to deal with the diagnostic and prognostic issues across the diverse medical realms. Also, ML algorithms are extremely used for medical data analysis, controlling inappropriate data, estimating disease breakthroughs, effective monitoring of patients, etc
Screening out various applications of ML shows how machine learning impact on society and deals with different aspects. The advancements and technical approaches Machine Learning such as Deep Learning, Graphical Models, and Reinforcement Learning allow us to take business decisions, optimize operations, augment productivity, and many more for industries to stand out in the market. Except for the examples discussed above, there are a number of ways where machine learning in daily life has been proving its potential.