The Applications Of Machine Learning

We have seen Machine Learning as a buzzword for the past few years, the reason for this might be the high amount of data production by applications, the increase of computation power in the past few years and the development of better algorithms.

Machine Learning is used anywhere from automating mundane tasks to offering intelligent insights, industries in every sector try to benefit from it. 

You may already be using a device that utilizes it. For example, a wearable fitness tracker like Fitbit, or an intelligent home assistant like Google Home.

But there are much more examples of ML in use such as prediction, image recognition, speech recognition, medical diagnoses and financial industry and trading.

To make things clear, herein, we share few examples of machine learning that we use everyday and perhaps have no idea that they are driven by machine learning.

1. Virtual Personal Assistants

Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants. As the name suggests, they assist in finding information, when asked over voice.

All you need to do is activate them and ask “What is my schedule for today?”, “What are the flights from Germany to London”, or similar questions. For answering, your personal assistant looks out for the information, recalls your related queries, or send a command to other resources (like phone apps) to collect info. 

You can even instruct assistants for certain tasks like “Set an alarm for 6 AM next morning”, “Remind me to visit Visa Office day after tomorrow”.

Machine learning is an important part of these personal assistants as they collect and refine the information on the basis of your previous involvement with them. Later, this set of data is utilized to render results that are tailored to your preferences.

Virtual Assistants are integrated to a variety of platforms. For example:

  • Smart Speakers : Amazon Echo and Google Home
  • Smartphones : Samsung Bixby on Samsung S8
  • Mobile Apps : Google Allo

2. Predictions while Commuting

RaTraffic Predictions: While we are using GPS, our current locations and velocities are being saved at a central server for managing traffic. 

This data is then used to build a map of current traffic. Plus, it also helps in preventing the traffic and does congestion analysis.

Machine learning in such scenarios helps to estimate the regions where congestion can be found on the basis of daily experiences.

However, the underlying problem is that there are less number of cars that are equipped with GPS.

3. Videos Surveillance

Imagine a single person monitoring multiple video cameras! Certainly, a difficult job to do and boring as well.

This is why the idea of training computers to do this job makes sense.

The video surveillance system nowadays are powered by AI that makes it possible to detect crime before they happen.

They track unusual behaviour of people like standing motionless for a long time, stumbling, or napping on benches etc.

The system can thus give an alert to human attendants, which can ultimately help to avoid mishaps.

And when such activities are reported and counted to be true, they help to improve the surveillance services. This happens with machine learning doing its job at the backend.

4. Social Media Services

From personalizing your news feed to better ads targeting, social media platforms are utilizing machine learning for their own and user benefits.

Here are a few examples that you must be noticing, using, and loving in your social media accounts, without realizing that these wonderful features are nothing but the applications of ML.

People You May Know

Machine learning works on a simple concept: understanding with experiences.

Facebook continuously notices the friends that you connect with, the profiles that you visit very often, your interests, workplace, or a group that you share with someone etc.

On the basis of continuous learning, a list of Facebook users are suggested that you can become friends with.

Face Recognition

Facebook just loosened the leash a little on its facial-recognition algorithms.

Starting 2017, any time someone uploads a photo that includes what Facebook thinks is your face, you’ll be notified even if you weren’t tagged.

It applies only to newly posted photos, and only those with privacy settings that make an image visible to you. Once Facebook identifies you in a photo, it will display a notification that leads to a new Photo Review dialog.

There you can choose to tag yourself in the image, message the user who posted an image, inform Facebook that the face isn’t you, or report an image for breaching the site’s rules.

5. Email Spam and Malware Filtering

There are a number of spam filtering approaches that email clients use. To ascertain that these spam filters are continuously updated, they are powered by machine learning. 

When rule-based spam filtering is done, it fails to track the latest tricks adopted by spammers. 

Multi Layer Perceptron, C 4.5 Decision Tree Induction are some of the spam filtering techniques that are powered by ML. Over 325, 000 malwares are detected everyday and each piece of code is 90–98% similar to its previous versions.

The system security programs that are powered by machine learning understand the coding pattern. Therefore, they detects new malware with 2–10% variation easily and offer protection against them.

6. Online Customer Support

A number of websites nowadays offer the option to chat with customer support representative while they are navigating within the site.

However, not every website has a live executive to answer your queries. In most of the cases, you talk to a chatbot. These bots tend to extract information from the website and present it to the customers.

Meanwhile, the chatbots advances with time. They tend to understand the user queries better and serve them with better answers, which is possible due to its machine learning algorithms.

7. Search Engine Result Refining

Google and other search engines use machine learning to improve the search results for you. Every time you execute a search, the algorithms at the backend keep a watch at how you respond to the results.

If you open the top results and stay on the web page for long, the search engine assumes that the the results it displayed were in accordance to the query. 

Similarly, if you reach the second or third page of the search results but do not open any of the results, the search engine estimates that the results served did not match requirement.

This way, the algorithms working at the backend improve the search results.

8. Product Recommendations

You shopped for a product online few days back and then you keep receiving emails for shopping suggestions.

If not this, then you might have noticed that the shopping website or the app recommends you some items that somehow matches with your taste. 

Certainly, this refines the shopping experience but did you know that it’s machine learning doing the magic for you?

On the basis of your behaviour with the website/app, past purchases, items liked or added to cart, brand preferences etc., the product recommendations are made.

9. Online Fraud Detection

Machine learning is proving its potential to make cyberspace a secure place and tracking monetary frauds online is one of its examples. 

For example, Paypal is using ML for protection against money laundering. 

The company uses a set of tools that helps them to compare millions of transactions taking place and distinguish between legitimate or illegitimate transactions taking place between the buyers and sellers.

10. Google

So it is obvious that Google eventually plans on fully integrating Machine Learning in all its operations. But that futuristic world is still a little far away!

For now, let’s see some of the ways in which Google currently uses Machine Learning so that we can understand the full scope of its applications in the future.

Google Translate

While it’s not exactly 100% accurate, it is still a great tool to convert text, images, or even real-time video from one language to another

And in case you wonder how it translates more or less accurately, well Google Translate uses Machine Learning of course!

It uses Statistical machine translation (SMT) which is a fancy way of saying that it analyses millions of documents that are already translated from one language to another and then looks for the common patterns and basic vocabulary of the language.

After that, it picks the most accurate translation possible based on educated guesses that mostly turn out to be correct

Google Photo

In case you are a millennial, I am sure you are a selfie addict! And of course, you use Google Photos a lot if you are an Android user as well.

Google Photos allows you to back up all your photos in a single location even if they were shot from multiple devices and it also offers lots of other cool effects using Machine Learning.

For Example, Google Photos also automatically creates albums of photos taken during a specific period without any input from you. 

And that’s not all, it can also select the “best photos”. And in case you haven’t sorted all your pictures into albums, you can also search for them by typing in names.

Suppose you want to find a picture with your cat, type in “cat” and you will get all the cat pictures

This is done using Image Recognition, wherein Deep Learning is used to sort millions of images on the internet in order to classify them more accurately.

So using Deep Learning, the images that are classified as “Cat” in your Google Photos are displayed.

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