Machine Learning & Its Application in Our Daily Lives
App Builder Appy Pie, March 07, 2018: There is a certain amount of excitement and quite a lot of buzz that is being created about artificial intelligence and machine learning where people have been talking quite enthusiastically about cars that would drive themselves through a zig zag of traffic, instant machine translations and more. While all these talks might sound far fetched and out of science fiction stories and films, we are rarely aware of how much of machine learning has actually found its way into our daily lives.
If you were looking for information on the topic of this blog and ended up here, you have probably made use of machine learning already! Whether you are booking a cab for your daily commute, catching up with your friends on your favorite social media channel, indulging in some online shopping, or looking something up on the web, you are definitely using some kind of artificial intelligence or machine learning to accomplish it.
What is Artificial Intelligence?
Artificial Intelligence has been defined as the intelligence demonstrated by the machines, in contrast to the natural intelligence displayed by the animal kingdom including the humans. AI or Artificial Intelligence makes it possible for the machines to learn from experience, adjust to new inputs, and perform tasks that humans would. Most of the examples of artificial intelligence that we hear about or encounter in our daily lives are dependent heavily on deep learning and natural language processing.
What is Machine Learning?
Having evolved from Artificial Intelligence, Machine Learning explores the study and construction of algorithms that can learn from and make predictions on data. Machine Learning is a field of study in Computer Science that gives the machines or computer systems the ability to learn with data, without the need of explicit programming. It focuses on developing computer programs that can access data and use it to learn for themselves. The basic idea here is to let the computers learn on their own without human intervention or assistance and adjust actions accordingly.
Applications Of Machine Learning In Our Everyday Lives
Machine Learning might seem like something that the scientists have been using for experiments in high tech labs or in top secret government missions and organizations, but it is in-fact a lot closer to our lives and more deeply enmeshed than we realize. Machine Learning could be at the base of something mundane that you do everyday and not even realize the connection.
Listed below are a few of the most common applications of Machine Learning that have become a part of the modern life without many people realizing it.
A) Personal Use
#1 Social Networking
On your favorite social media channel, you must have come across a personalized newsfeed, targeted sponsored content or ads and other such features quite often. This is where the social media channels are making good use of machine learning for their own benefit and for the benefit of their users. Let’s take a quick look at some of the most popular features that might have amazed you initially before you fell in love with them, without even realizing that they are all based on machine learning.
Facebook: One of the earliest machine learning or AI feature on Facebook was face recognition, where the service would automatically highlight the face of a friend in your pictures, before offering you a suggestion to tag. Artificial Intelligence also comes to play when Facebook personalizes your newsfeed in order to make sure that what you see, is what you want to see, what you are interested in, instead of just some random popular or trending posts. Sending out targeted ads is also possible only through AI, and has proved to be one of the most effective features on the platform. Another interesting machine learning enabled feature is the ‘People You May Know’ feature where you get friend suggestions based on the existing social circle you have and the social circle of your friends, your hometown, your alma mater and many other such factors.
Instagram: One of the most popular social media channels is Instagram – a photo sharing app makes great use of machine learning in order to identify the contextual meaning of emojis that have, to a great extent managed to replace slang words e.g. ‘lol’ has successfully been replaced by laughing emojis of a variety. The constituent algorithms in the app make it possible to identify the sentiment behind a certain emoji before creating or auto-suggesting apt emojis or emoji hashtags. Superficially this might seem like a trivial application for Artificial Intelligence, but it is only after seeing a whopping rise in the use of emojis across all major demographics that Instagram released this feature. It is the ability to interpret and analyze this phenomenon at a large scale through emoji to text translations that can prove to be the foundation for deeper analysis of the way people have been using Instagram.
Pinterest: The image based social media channel makes use of computer vision, an AI application that teaches the computers to virtually ‘see’ so that they can identify the objects in images in order to recommend or suggest similar pins. On Pinterest there is an array of machine learning based applications including spam prevention, search & discovery, ad performance and monetization, in addition to email marketing.
#2 Voice to Text
We don’t even stop to think twice about the voice to text conversion feature in our smartphones. This could be initiated with a tap on the screen or saying a particular phrase. All you do then is begin speaking and the phone then converts whatever you say into text in real time. This might have taken on the status of a commonplace feature, almost mundane, but for quite a long time accurate automated transcription was a challenge even for the most advanced computers.
#3 Virtual Personal Assistants
Among the virtual personal assistants, you might recognize names like Siri, Alexa, Google Now, and Cortana as they have now become a part of our lives. When we say virtual personal assistant, you know what we mean, right? They basically do the task of finding you the information that you ask for over voice. All you have to do is activate it and ask them for the nearest pizza place, show times for the latest film release, directions to the nearest pharmacy or any such questions. The virtual personal assistant will then look out information, recall your related queries, or send a command to other resources including the apps you have installed on your device to collect information and formulate cohesive answers for you. For certain tasks you can also instruct the assistants like setting wake up alarms, reminders for super important meetings etc.
For these assistants to work well, machine learning assumes great importance as the assistants gather and hone the information on the basis of your past interactions with them. This set of data is, at a later time put to use to come up with results that would be tailored to your specific preferences.
Largely due to the accomplishment of high accuracy voice-to-text technology, we have the scope to depend heavily on it for basic conversation which has led to it emerging as the control interface for a new generation of virtual personal assistants.
The basic avatars of virtual personal assistants who could perform your internet searches for you, set reminders on your device, and carry out integrations with your perform acted as the base for the more evolved models from Amazon viz. Alexa & Echo. Microsoft too is a part of the machine learning bandwagon with Cortona, its assistant that comes pre-loaded on Windows computers and Microsoft smartphones.
B) The Daily Commute
#1 Predicting The Traffic
Have you been taking the GPS navigation services for granted, or have you given it a little thought? When we use this service, our real time location and the speed with which we are travelling are taken into account and saved at a central server to manage traffic. It is this data that is then used to build a map of the real time traffic conditions. Google Maps for example has the ability to analyze the traffic speed at any point in time. Now that they have acquired Waze – the crowdsourced traffic app they can even incorporate the user-reported traffic incidents including accidents, construction etc. with great ease. It is this massive influx of data that is then fed into its proprietary algorithm that makes it possible for Google Maps to suggest the shortest possible route for you and reduce your daily commute times. It is essentially machine learning that helps in the estimation of the regions where congestion may be found on the basis of daily experiences.
#2 Online Transportation Network
As you book your cab on Uber or Lyft, they give you a near accurate estimate of the fare and the waiting time for you. When you book these services on a share basis, how do they give you these estimates, how do they minimize these waiting times, and how do they match you with other people while minimizing the detours? Quite unsurprisingly, the answer to all these questions lie with Machine Learning (ML). At Uber, it is through Machine Learning that the rider demands are predicted, especially while defining the surge prices.
#3 AI Autopilots
The AI autopilots in commercial airlines is one of the earliest examples of successful use of artificial intelligence and has been in use since as far back as 1914, if you were to get a little flexible about defining autopilot. The scale of the disruption today can be understood by the fact that on an average Boeing flight schedule, there is only the seven minutes of flight time that involves human steering, which is primarily the takeoff and landing of the flight.
#1 Spam & Malware
You might have written email off as a traditional or conventional system with minimal evolution or innovation, but did you know that AI and machine learning play a big part in here. One of the strongest applications of this technology is the spam filter that is ‘learning’ perseveringly from a great number of signals including words in messages, message metadata etc.
Did you know that around 325,000 malware are detected every single day and each of the codes are 90-98% similar to the past versions? Machine learning help the system security programs understand the coding pattern which means that they have the ability to detect new malware with 2 to 10% variation with great ease and offer protection against them.
#2 Categorization Of Mails
The top tabs on Gmail that categorize your mails into primary, social, and promotions tab while labeling certain emails as important are possible only with the use of AI. Whenever you mark a ceratin mail as important or unimportant you are letting Google know of your preferences, this adds on to their ‘learning’ experience, thus helping them refine their smart categorization, saving your precious time and de-cluttering your inbox.
D) Online Customer Care
More and more people are finding comfort in getting chat support, which means that they are free to multitask and not be tied down to just the one task. Hence, a large number of websites offer their customers the option to chat with a customer care executive while they are exploring the website. However, not all these companies would have live executives to answer the customer queries. Most of the websites use their chatbots in order to handle the common queries and pull out information from the website and offer a solution or an answer to the customers. With time, the chatbots would have to understand the customer queries and concerns a little better in order to serve the customers better, and that can only be accomplished through machine learning.
E) Online Shopping
If you are an ardent online shopper, you must have noticed that your favorite online shop knows just what you’d like and makes apt suggestions. This essentially refines the whole shopping experience for you, and the shopping portal quickly becomes your friend and confidante who would always know just the thing you want! This is all done through machine learning! Your product searches yield more relevant results for you as do the recommendations. Not just that, machine learning helps prevent credit card or other payment frauds as well!
F) Banking/Financial Institutions
#1 Depositing Checks on Mobile
Banks have begun offering the service to deposit checks through the smartphones, thus eliminating the need to physically visit the bank in order to deliver a check. This has been made possible through the use of AI & ML which can decipher and convert the handwriting on the check into text through OCR.
#2 Fraud Prevention
Detecting and identifying a fraudulent transaction is of extreme importance for a financial institution. Due to the sheer volume of the transactions that happen on a daily basis, it is impossible to review each one of them manually! Through machine learning however, systems can be created to learn which transactions would fall under fraudulent transactions
#3 Credit Decisions
You apply for a loan or credit card to a financial institution and they have to perform a credit check to determine whether they can accept your application and what kind of interest rate or credit limit can they offer you. The ever-familiar FICO score is developed by using machine learning in order to determine the specific risk assessment for individual customers.
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