In our blog on the applications of Machine Learning, we will start by looking at Google Maps’ traffic prediction.
Google Maps’ Traffic Prediction
We will start with one of the Machine Learning applications that we use in our day-to-day life, i.e., Google Maps’ traffic prediction. Google Maps is very accurate in predicting traffic. If you have an Android phone or an iPhone with Google Maps opened and services enabled on it, then your phone or the app anonymously sends real-time data back to Google. Then, Google uses this information or data to calculate how many cars are there on the road or how fast they are moving. So, as more people start using the app, the traffic data becomes more accurate.
Google has also incorporated the traffic data from an app called ‘Waze.’ The company bought it for $1 billion in 2013. The app monitors traffic reports from the local department of transportation. Google even keeps a history of traffic patterns on a specific road so that it can predict the traffic at a specific time on a specific location.
If traffic is more, the app would suggest a faster route to reach your destination on time. If you are feeling like Google is monitoring and tracking your every move, then you can opt-out anytime by turning off the location services. But what if everyone did that? Well, Google would be in trouble then as the data and the result might not be accurate. This is the reason why at some places Google Maps is not so accurate. So, this was about Google Maps using a Machine Learning algorithm to analyze and predict the result using your data. More data you feed more accurate it becomes!
Now, moving ahead with the applications of Machine Learning, we will look into Google Translate.
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When we talk about the applications of Machine Learning, Google Translate enables us to translate documents, sentences, and websites instantly. All these translations come from computers that use statistical machine translation. These translations are generated by computers based on the text patterns found.
For teaching someone a new language, we usually start off by teaching the vocabulary, grammatical rules, and then explain about constructing sentences.
Similarly, when a computer learns a new language, it understands that language by referring to the vocabulary and the grammatical rules. But learning a new language is very complicated because of the exceptions that exist in any rule. When you combine all of these exceptions in a computer program, then the quality of the translation begins to breakdown.
Now, when it comes to Google Translate, it takes a slightly different approach. Instead of teaching every rule of a language to the computer, what it does is, it lets the computer find the rules by itself. Google Translate does this with the help of Machine Learning. This is done by examining billions of documents that are already translated by human translators.
Google Translate collects text from multiple sources. After the text or the data is collected, the machine tries to find patterns by scanning the text. Once the machine detects the pattern, this pattern is used multiple times for translating similar text. Repetitions of the same process by the machine will detect millions of patterns that will make the machine a perfect translator.
For a few languages, Google Translate already has translated documents. Therefore, only a few patterns are to be detected by the software. Well, this may end up with a varying translation quality.
Google’s translation is undoubtedly perfect, but by constantly providing newly translated text, it can get smarter and translate better. This is how Google translates your speech.
Now, we will move on to the Machine Learning applications by looking at Facebook’s Automatic Alt Text.
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Facebook’s Automatic Alt Text
Facebook’s Automatic Alt Text is one of the wonderful applications of Machine Learning for the blind. Facebook has rolled out this new feature that lets the blind users explore the Internet. It is called Automatic Alternative Text. With the help of this, the blind are getting the tools by which they can experience the outside world and the Internet.
So, the applications of Machine Learning have expanded a lot, and it is changing the way of experiencing the world with the use of technology.
A normal person can see an entire screen full of information, and with that they can make decisions. But, for a blind person, it is a bit more difficult. They use screen readers that help in describing websites or apps. Also, they use keyboard shortcuts for jumping into a page. These screen readers usually look at the website code instead of what is visible on your screen.
Now, social media is a part of our lives. Facebook has estimated that there are more than a billion photos shared every day. However, the pictures shared would be of no use for the blind if they don’t come up with the text that outlines the picture.
So, Facebook is resolving this problem with the help of ‘Automatic Alt Text.’ Here, when the built-in reader is turned on and when we tap on a picture, then Facebook’s Machine Learning algorithms try to recognize the features of the image and then create an alt text. This alt text will describe the picture with the help of the screen reader.
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For example, if the picture shows a couple as shown in the above image, the alt text could be like ‘There are two people smiling, wearing sunglasses, ocean.’ Facebook is trying to make this description, the alt text, more precise and narrative.
Recently, Twitter has also added a feature that makes use of alt text for images.
This was all about the applications of Machine Learning which Facebook developed to help the blind experience the world.
Further, in this blog on ‘Applications of Machine Learning,’ we will see another application of Machine Learning, that is, Amazon’s recommendation engine.
Amazon’s Recommendation Engine
How do you think Amazon recommends your next product? Well, the answer is, Machine Learning with Big Data. It involves three stages: events, ratings, and filtering.
In the events phase, Amazon tracks and stores data regarding customer behavior and their activities on the site. Every click the user makes is an event, and the record of the user is logged in the database. The entry will be stored as something like ‘the user A clicked on the product X once.’
This way, different types of events are captured for different kinds of actions like a user liking a product, adding a product to the cart, or purchasing a product.
Next phase is ratings. Ratings are important as they reveal what the user feels about the product. The recommendation system then assigns implicit values on different kinds of user actions like four-star for purchase, three-star for like, and two-star for a click, and so on.
In the applications of Machine Learning, Natural Language Processing plays an important role.
Amazon’s recommendation system also uses Natural Language Processing to analyze the feedback which is provided by the user. The feedback can be something like ‘the product was great but the packaging was not good at all.’ With the help of Natural Language Processing, the recommendation system calculates the sentiment score and then classifies the feedback as positive, negative, or neutral.
Now, the last phase is filtering. In this step, the machine filters the product based on the ratings and other user data. The recommendation system uses different kinds of filtering such as collaborative filtering, user-based filtering, and hybrid filtering.
Collaborative filtering is one in which all the users’ choices are compared and they get a recommendation. For example, a user X likes products A, B, C, and D, and the user Y likes products A, B, C, D, and E. So, there is a chance that the user X will also like the product E, and the machine will recommend the product E to the user X.
After that comes the user-based filtering. In this, the users’ browsing history such as likes, purchasing, and ratings are taken into account before providing the recommendation.
Finally, in hybrid filtering, there is a mix and match of both the collaborative and the user-based filtering.
So, this is how Amazon recommends products for you. The applications of Machine Learning are not limited to just Amazon; organizations such as Alibaba, eBay, and Flipkart also use the same approach.
Going ahead in this blog on ‘Applications of Machine Learning,’ we will see about spam detection in Gmail.
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Spam Detection in Gmail
This is one of the common applications of Machine Learning that we encounter in our day-to-day work. Spam detection makes use of filters. Algorithms are regularly updated based on the new potential threads found, advancement in technology, and the reaction given by users to spammed mails. Spam filters remove the threats using text filters based on the sender’s history. So, in this blog on applications of Machine Learning, next, we will see text filters, client filters, and spam filters.
The first one is a text filter. Text filters work by using algorithms to detect words and phrases that are used in spam mails. Phrases like ‘Lottery’, ‘You won!’, and ‘Free Bitcoin’ are often an immediate flag for removal by filters. Spammers use a misspelling or substitute characters to pass through these filters. Fortunately, the modern spam filters can also make allowance for these types of misspellings; if a word comes up even with a character substitution, the message will still be blocked. Moving ahead with filters in applications of Machine Learning, we will see how a client filter works.
In addition to the simple text filter algorithm, the spam filter uses client identity and history to block malicious and annoying spam mails. This is done by looking at all messages a user has sent out. If a user sends a huge amount of emails constantly or several of his/her messages have already been marked as spam through the text filter, then that user’s emails will often be blocked entirely. So, this brings us to the use of the blacklist.
Spam filters also include a blacklist. Blacklisting is simply a process of making a list of the known email addresses of a spammer. This list prevents any inbound messages to come from those email addresses going forward. If you have something marked as spam, then it will directly go to the blacklist. It may also help Google or Gmail to make a note of certain keywords from that spammed mail. So, that’s the outline of spam detection and how Gmail understands which email is spam. However, the real-time processes are a lot more complex and it consumes a lot of data. It is also used in fraud detection.
After spam detection in the applications of Machine Learning, we will move on to Amazon Alexa, which is another wonderful application of Machine Learning.
The device shown in the below image is Amazon Echo, and the brain or voice of Echo is known as Amazon Alexa. It is capable of doing several tasks such as giving the weather report and playing your favorite song. Also, the word ‘Alexa’ is a wake word. As you say this word, it starts the recording of your voice. When you finish speaking, it sends the voice to Amazon. The service that persists this recording is called Alexa Voice Service or AVS, and it’s one of the magnificent applications of Machine Learning. This service is run by Amazon.
AVS interprets the command from the recorded audio. It is also called a voice detection service that can work with many other online services.
They want this application of Machine Learning to expand more, and that is why they provide this service for free so that others can build products or services on top of this service.
The commands interpreted by Alexa can be like asking for time and weather reports. After the command has been noted, it is sent to Amazon. Then, AVS gives the response by telling you the time and weather reports with the help of an audio file sent by Amazon servers.
You can also give some complex tasks such as if you tell Alexa to tell you the ‘Applications of Machine Learning,’ then AVS will search the keywords you have set up (i.e., Applications of Machine Learning) in servers. Then, it responds by sending a command back to Echo, which tells you the applications of Machine Learning.
Alexa can also control your home appliances by voice commands if you are using smart electronic devices such as Philips smart bulbs. You can give commands to Alexa to switch on or off the lights. You can even link it to Domino’s and order pizza by giving commands to Alexa. Isn’t it a magnificient application of Machine Learning?
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Echo and Alexa can perform a lot of things. Amazon is continuously adding more skills to Alexa that will make it better.
However, for Alexa to work, there should be Internet connectivity and AVS. If there is no Internet connectivity, then Alexa won’t be available.
So, this is one of the wonderful applications of Machine Learning.
Well, if you consider these products, Amazon is not the only company in the market that has used this application of Machine Learning. Google uses ‘Ok Google’ as its voice command services, Apple uses ‘Siri,’ and Microsoft uses ‘Cortana.’ Even if they are using the same approach, i.e., voice commands processed in cloud servers, they are not as good as Alexa.
Now, we will move on to another superb application of Machine Learning, i.e., self-driving cars!
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Tesla’s Self-driving Cars
Tesla’s self-driving cars is another of the most-used applications of Machine Learning. A recent study has shown that over 90 percent of the road accidents are caused by human errors, and these mistakes are often catastrophic. The accidents have led to a massive number of unnecessary deaths; lives that could have been saved if they were driving safely. This is where the self-driving cars come into the picture.
Thus, this real-world application of Machine Learning has led the automobile industry to a new and safer direction.
These self-driven cars are autonomous cars that are safer than the cars driven by humans. Things that make these cars safe are that they are not affected by factors like illness or emotions of the driver.
Self-driving cars persistently observe the environments and scan all the directions and make their move. Due to their mechanism of not lagging in observation, self-driving cars work perfectly.
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Working of Self-driving Cars
Self-driving cars are a real-world example of Machine Learning that mainly uses three different technologies: IoT sensors, IoT connectivity, and software algorithms.
When we talk about IoT sensors, they help in making self-driving cars a reality. There are sensors for blind-spot monitoring, forehead collision warning, radar, camera, and ultrasonic sensors. All these help in the navigation of a self-driving car.
Next is the IoT connectivity. These autonomous cars use Cloud Computing to fetch traffic data, surrounding conditions like weather conditions, vehicles adjacent to them, and many other factors. This helps them observe their surroundings and make a conscious decision. These self-driving cars should be connected to the Internet all the time.
Finally, we have the software algorithms that make them work. So, the data that is collected by the car is analyzed such that it decides the best scores of action. This is the key function of the algorithms and the software. This is done with the help of Machine Learning, and it is a complex task as it has to make decisions perfectly.
If we talk about self-driving cars, it is not limited to Tesla. In today’s world, the most famous self-driving cars are those made by Tesla and Google. Tesla cars work by examining the surroundings with the use of a software system that is the auto-pilot. As we use our eyes to visualize the surrounding world, the auto-pilot does it with the help of hi-tech cameras for recognizing objects. After that, it interprets the information and then makes the best conclusion out of it.
This major application of Machine Learning is revolutionizing the automobile industry. This is all about how self-driving cars work using the technology of Machine Learning.
Next, in this blog on the applications of Machine Learning, we will look at the Netflix movie recommendation system.
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Netflix Movie Recommendation
More than 80 percent of the TV shows that we are watching on Netflix are discovered through the platform recommendation system. It means, the majority of what we decide to watch on Netflix is a result of the decisions made by the mysterious black-box algorithm.
How does the Netflix recommendation system work?
Netflix uses Machine Learning algorithms to recommend the list of movies and shows that you might have not initially chosen. To do this, it looks at threads within the content. There are three legit tools for Netflix, and they are as follows: the first is Netflix members, the second is taggers who understand everything about the content, and the third is the Machine Learning algorithms that take all of the data and put them together. Netflix has more than 100 million users around the globe. If you count the multiple user profiles for each subscriber, it comes to a total of around 250 million active profiles.
Netflix uses different kinds of data from these profiles. It keeps track of what you guys are watching from your profile, what you watch after completing your current video, and even what you have watched earlier. It also keeps track of what you have watched a year ago or what you are currently watching, or at what time of the day you are watching. So, this data is the first leg of the metaphorical tool.
Now, they are combining this information with more data to understand the content of the shows that you are watching. This data is gathered from dozens of in-house and freelance stuff watched every minute and every show on Netflix, and they tag them. All the tags and user behavior data are taken and fed into a very sophisticated Machine Learning algorithm that figures out what’s the most important.
Well, these three legit tools are used to analyze the taste of communities around the world. It’s about people who watch the same kind of things that you watch. Viewers are made to fit into thousands of different taste groups that affect recommendation pop-ups on their screen at the top, as an interface with joined rows of similar content.
Across the globe, the tags used are the same for all the Machine Learning algorithms. The data Netflix feeds into its algorithms can be broken down into two types: implicit and explicit.
Explicit data is what you literally tell. For example, you give thumbs up to Friends and Netflix gets it.
Implicit data is real behavioral data. It’s like, you did not explicitly tell Netflix that you like Black Mirror, but you just watched it in two nights. So, here Netflix understands the behavior. But, just as a matter of fact, the majority of useful data is implicit data.
This is how Machine Learning is used in Netflix recommendation system, and it is one of the applications of Machine Learning.
There are a lot more real-world applications of Machine Learning, but those described in this blog are the major applications of Machine Learning.
In this blog, we have seen various Machine Learning applications which are revolutionizing the world. We are moving into the next generation with the full-fledged Machine Learning technology that will help in giving a whole new direction.
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