8 Machine Learning Applications in our Daily LivesMarch 12, 2020
Machine Learning is widely used across a number of industries. It has become an integral part of our everyday lives. You use Machine Learning integrated applications multiple times on an everyday basis, however, you generally do not realize that those applications use this technology. Here, you will learn about some of the most commonly used Machine Learning Applications. First, let’s get an overview of the Machine Learning technology.
What do you mean by Machine Learning?
Machine Learning (ML) is among the most popular technology today. It is a subset of Artificial Intelligence (AI) that enables machines to gain information and make improvements on the basis of past data that is made available to them. It uses Deep Learning algorithms to assist machines in the learning process. With an increase in the quantity of data fed to these systems, there is an improvement in the algorithms which makes them smarter and better equipped.
Artificial Intelligence (AI), on the other hand, is a superset of ML. It uses complex ML and Deep Learning algorithms along with Natural Language Processing (NLP) to simulate human intelligence in computers and machines. Once the systems learn and get smarter with the help of Machine Learning, Artificial Intelligence helps them think and act like humans in certain situations.
Deep Learning is a subset of Machine learning that makes use of neural networks to give machines the ability to mimic the human brain with all its complexity.
In order to learn in-depth about ML, sign up for an online Machine Learning course wherein you will learn the significant concepts of this technology.
Both Machine Learning and Artificial Intelligence have become a core part of almost every industry today. They have taken over almost every industry including healthcare, banking, telecommunication, automobile, education, and finance.
Here, you will come across some of the most common applications of ML in our day-to-day lives.
Top ML Applications
Machine Learning and Artificial Intelligence surround us from all corners. Almost every industry today is using these technologies to perform various activities and also to automate redundant tasks for accuracy and efficiency. There are high chances that you use these technologies on a regular basis without any clue since most devices, services, and products are enhanced with the implementation of AI and ML in them. To know how you can apply ML in real-life applications, you can also check out the comprehensive Machine Learning Tutorial.
So, let’s now read about the top Machine Learning applications.
Virtual personal assistants like Amazon’s Alexa, Apple’s Siri, Google Now help find information when asked or commanded using voice. These assistants use speech recognition to understand human language and provide the necessary result. You just have to activate them and ask the questions to get information. In order to answer you and offer relevant information for your queries, these assistants search for data, refer to your earlier queries that are related, or send respective commands to other devices to gather data. Moreover, you can also instruct them to perform certain processes or tasks like setting alarms and reminders.
Speech recognition allows these assistants to translate voice to text. You can also refer to this technology as automatic speech recognition (ASR), speech to text (STT), or computer speech recognition. They use ML to gather and refine data based on your past involvement and information stored. Further, this data set can also be used to tailor the results as per your preferences and offer better and more accurate results.
Virtual Assistants are integrated into numerous platforms including smart speakers like Amazon Echo, mobile applications such as Google Allo, and smartphones like Samsung Bixby.
You can integrate Machine Learning in tools and devices that can assist in the diagnosis of any illness. It offers techniques and methods that can help solve numerous prognostic and diagnostic issues in the medical industry. It helps to analyze significant clinical parameters along with their prognosis combinations to extract medical information for research, to monitor patients, and to plan therapy.
Machine Learning also assists in the process of data analysis in terms of detecting data regularities by dealing with unstructured data, depicting the continuous data used in ICUs (Intensive Care Units), and for effective health monitoring purposes. Besides, the successful implementation of Machine Learning in the sector of medicine can aid the integrating process of machine-based systems in the healthcare department. Further, it allows medical experts to facilitate and enhance their work thereby, improving the quality and efficiency of the healthcare department.
It is not practical for an individual to monitor numerous video cameras. It is difficult, boring, and a redundant job which is why it is more ideal to train computer systems to complete this job. With machines doing this work, the results will be more efficient and accurate due to the reduction of possible human errors.
Today, most surveillance systems are integrated with ML and AI which enables them to detect crimes even before they occur. These systems generally look for unusual human activities and behavior such as stumbling or standing idle and motionless longer than necessary. By tracking these actions, systems can alert human attends in case of any suspicious activities with the help of which, you can avoid any mishaps and criminal activities from taking place. Besides, when these events are reported to be true, they improve the surveillances’ ability to detect such activities due to Machine Learning.
Email Spam Filtering
Clients use a variety of filtering techniques and approaches for spam emails. The spam filters use ML to ensure that they are regularly updated. When you filter the spam on the basis of specific rules, it generally fails to identify the new tricks acquired by spammers. Some of the commonly used techniques that are integrated with ML to filter spam emails include Decision Tree and Perceptron.
On a daily basis, more than 325,000 malware or malicious software are detected and all the codes are 90–98 percent the same as their older versions. The security programs of the system can identify the coding pattern with the help of the Machine Learning technology. Also, this enables the programs to easily detect malicious software with a varied range of 2–10 percent and offer protection against the same.
Prediction of Traffic
All of us use GPS services to navigate places. When we use these services, our locations and speed are generally saved in the main server. Further, this information is used to create a map of the current traffic with the help of Machine Learning. You can use this map to avoid traffic and congestion. However, the only drawback of this system is that it might not necessarily be accurate all the time as not all vehicles are equipped with GPS navigation systems. In such cases, ML estimates the regions in which there is congestion regularly.
Most of us have used online applications such as Ola and Uber to book cabs. These applications interpret the estimated time of arrival (ETA) to the destination and the price of the trip while booking itself. They have the ability to make such predictions due to Machine Learning. They also use this technology to surge prices based on the current time and demands.
One of the coolest ML application is self-driving cars. As the name suggests, these are cars that have the ability to drive on their own. ML plays a significant role in the development and working of these cars. Tesla, one of the leading organizations, has developed autonomous cars using Machine Learning, Deep Learning, and Artificial Intelligence. These technologies help make sense of the surrounding while driving and also makes predictions of the behavior of other vehicles.
Social Media Services
Social media platforms like Facebook and Instagram use Machine Learning for their benefit and also for the benefit of their users. This technology helps these platforms in numerous ways including personalizing your feed and targeting ads. There are several features on these platforms that you utilize daily without realizing that they are ML applications.
People You May Know
ML works on the concept of learning with experiences. These social media platforms keep a track of the friends with whom you connect, your interests, profiles that frequently visit, and the place you work. On this basis, these platforms learn and suggest to people that you may recognize or be friends with them.
Generally, when you upload pictures with or of your friends on these platforms, they recognize the friend. They notice the features, projections, and poses and match them with the people on your friend list. The overall process is extremely complex, however, it seems like a simple ML application in the frontend.
Most web pages offer chat support via representatives when users visit their site. Yet, they all don’t need to have live executives 24*7 hours to help solve your problems. Usually, you and most other users communicate with chatbots that extract information from the site and offer the same. They learn with experience and time. They use ML algorithms and Natural Language Processing to understand the human language along with the tone used and respond accordingly.
ML helps in making cyberspace secure by tracking online monetary frauds. E-money applications like Paypal use this technology to protect its users from activities like money laundering. They utilize various tools to compare tons of online transactions that take place. Further, with the help of these transactions, they make distinctions between legitimate and illegitimate money transactions that occur between two parties.