Machine Learning Applications

Machine Learning has gained such a wide recognition and acceptance that many industries are now utilizing it. If you are still new to the concept of Artificial Intelligence, you may not have known that many things you use are all dependent on it. The most interesting thing is that large corporations are now using the efficiency that characterizes the operations of ML.

Some of the industries that are actively implementing Machine Learning are:

  • Marketing & Sales – Companies in this sector apply ML in analyzing their customers purchasing history so that they can recommend products to customers. With such ability to provide personalized customer shopping experience, the future of sales & marketing is looking brighter.
  • Finance- ML enables companies to prevent financial frauds and identify investment opportunities.
  • Government- Utilities agencies and public safety agencies use ML to mine their multiple data.
  • Healthcare – This sector is using ML to make wearable sensors and other devices that can be used to access the health of patients.
  • Transportation – With ML, transportation companies can predict problems that may arise from certain routes and inform the customers to avoid those routes.
  • Oil & Gas – Companies under this sector use ML to analyze different underground minerals (Flatworld Solutions Pvt. Ltd, 2018).

With the increasing success in the application of Machine Learning in these sectors, other industries are likely to join very soon. Apart from these industries, there are specific areas where the application of ML is more pronounced.

Self-driving Car

This is one of the applications and marvels of Machine Learning in this era. A self-driving car moves without a human driver. It is built to sense and master its environment, observe the safety rules and move without assistance. Other names for these cars are driverless cars, robot cars, auto or autonomous cars (Wikimedia Foundation Inc., 2018).

Robot cars have many sensors which help it to operate around us. They are built with computer vision, GPS, radar, sonar, Lidar, inertial measurement units, and odometry. Also, these self-driving cars have control systems, a sensory information system which helps them to identify the paths that will be appropriate for them to use and also a signage system which enables them to avoid obstacles.

Although the idea of using manufacturing robot cars is still not legally solid yet, many partially-autonomous cars & trucks are already available.

Many companies like Uber, Nissan, Google, Tesla and other auto-makers, tech companies and researchers have developed different types of self-driving technologies. All these systems have one thing in common; an internal map which they use to know their surroundings. For instance one of the largest global transport company Uber, has a self-driving prototype that uses 64 laser beams with other sensors for their internal map (Union of Concerned Scientists, 2018).

The point here is that these technologies have what it takes to work on their own as programmed by engineers. However, there may be situations when the need for human intervention arises especially when the systems face an uncertain situation.

Potential benefits of self-driving cars

  • Low cost – It is expected that the cost of operating an auto car will be lower than the human-driven vehicles we have today. Although we can’t be too sure yet because we may need more information about how they will operate, their maintenance and repairs, to agree to the hypothesis.
  • Increased safety – It is also expected that robot vehicles will help to reduce the number of fatalities from auto crashes every year. The expectation is that driving software could be more accurate and alert to danger signals on the road than human drivers. At least, the deaths from DUI drivers can be eliminated. However, certain concerns like cyber security may impede the safety benefits.
  • Increased mobility – Imagine a car that children can use without a human driver. People like the senior citizens that have long past the age of driving will also find the development interesting. Also, the poor and disabled can also use these robot cars without the need of employing a human driver.
  • Customer satisfaction – Auto vehicles are expected to increase the satisfaction of customers because it will eliminate many of the hassles which car owners face. Imagine a time when you can relax in the back seat and allow your car to take you to your destination. If you can avoid all those navigations you do during traffic jams, won’t it be wonderful? What about the irritating parking tickets and the lack of parking spaces when you go out? All these challenges are expected to be history with the advent of self-driving cars.
  • Lower crime rate – Many criminals use their cars to perpetrate all sorts of evil. Criminal acts such as robbery, kidnapping etc. could be lower with these auto technologies. Also, since the robot vehicles will be tracked and monitored, it will be easy to determine their position at every point in time (Wikimedia Foundation Inc., 2018).

Although the autonomous vehicles are expected to offer many benefits to the people, there are still some areas of concerns and potential problems. It is assumed that having these driverless vehicles can have issues with safety, government regulations, lack of privacy, hackers and terrorist attacks etc.

Facial Recognition

This is another Machine Learning application that has been helpful in recent times. This system can verify or identify someone by analyzing the features of the person’s face. The technology behind Facial Recognition (FR) compares the facial features of a target from an image with the faces already stored in its database (Wikimedia Foundation Inc, 2018).

The facial recognition software was first used as a computer application, but nowadays, it has been incorporated in robotics, mobile phone and other types of technology. Many of the mobile devices we have today have high-quality cameras that use facial recognition for identification and authentication. Some of the phones where the use of FR is pronounced are: Apple iPhone X which has a face identification technology that requires the owner to use his/her faceprint to unlock the phone.

Other companies such as Google and Amazon are offering many services that clients can use to analyze images. With Amazon Recognition from Amazon AI suite, you can add the FR and Analysis feature to any application you are developing. Also, with Google Cloud Vision API, you can add the facial recognition feature too. If you are familiar with Kinet Motion gaming system, you will know that it uses FR to differentiate players.

What about our Airports? They are using smart advertisements that can easily identify people’s gender, age and ethnicity so that they can offer more targeted advertisement to customers based on their demography. We are already familiar with Facebook tags. This feature is possible because of the facial recognition software.

The application of Facial Recognition as a bi-product of Machine Learning may not be very vast as of now. It is expected to be more popular as the years go by. Many countries are adopting it and using it as a form of identification and verification. Also, it is being applied as a marketing tool in the business sector.  Many other applications such as video surveillance, video databases, automatic image indexing and others are making Facial Recognition popular in recent times.

One of the advantages of Facial Recognition is that it can identify its target without his/her cooperation. For instance, some of the systems in public places, airports and multiplexes can identify anybody in a crowd, and nobody will know. However, the efficiency of facial recognition can be hampered with factors such as facial expression, illumination, noise and pose (Search Enterprise AI, 2018).

Email Spam and Malware Filtering

This is another application of Machine Learning that aims at protecting Internet users. Have you noticed that sometimes, you receive emails from sources or companies you haven’t visited or heard about in your entire life? These emails are called spam emails. They can be irritating and space-consuming because you never asked or wanted them. You may wonder how these companies get your address. The truth is that they use a program called Spambots to gather many email addresses from the Internet. One of the dangers of spam emails is the type that comes as phishing emails. You receive them thinking they are from a legit company or your financial institution. If you mistakenly enter your details or banking details, the online thieves will use it to defraud you.

Malware, on the other hand, are spyware and viruses that come into your computer from online sources you visited or pop-ups which you mistakenly clicked online. One of the dangerous malware online is spyware which targets your personal and confidential information. Once it gathers them, it will send them back to the author; the person who wrote the malware (Rouse, Email Spam, 2018).

Many people have fallen victim to malware attacks because as far as you are an Internet user, you will be exposed to such attacks. The good news is that some Machine Learning systems can now handle spam and malware by filtering your emails. Many approaches exist to spam filtering, and with ML, these filters are always updated without fail. Some of the Machine Learning techniques for spam filtering are C 4.5 Decision Tree Induction and Multi-Layer Perceptron (A Medium Corporation Inc., 2018).

Online Customer Support & Product Recommendations

Online customer support is another ML application which you have been using without knowing its origin. Sometimes when you visit a website, or you want to make inquiries online, you will get an option to discuss your concerns by chatting with their customer support officer or representative. It is always amazing how helpful these chats can be and how available they always seem to be. The truth is that not all the websites have an executive handy as you think.

In most cases, the representative attending to you is a chatbot. These bots will give you the correct answer to your queries because they collect the right information from the website and give to you in answer to your question. As time passes, these bots will improve and understand what the customers want well — all as a result of Machine Learning.

The next application of Machine Learning is the product recommendation services which we enjoy. At least, you must have received one email or the other making recommendations for products to buy. Most of these recommendations can surprise you because they will match your taste and needs. So how did the company know what you may need or enjoy? It is all based on Machine Learning. The shopping app recommends products to you depending on your buying behaviour and activities online. You may not know it, but they are constantly working to make your shopping experience worthwhile.

There are many other Machine Learning applications such as fraud detection, search engine result refining, social media services, video surveillance, Predictions, Virtual Personal Assistants, etc (A Medium Corporation Inc., 2018).

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