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Who's using ML?

Exploring the Applications of Machine Learning

Gaurav Belani
© Shutterstock / treety

Today, machine learning has expanded far beyond simple games of checkers. Although there is still room for innovation, this technology has seen tremendous improvement. And with its many applications, it is moving into the public consciousness as well.

Machine learning isn’t a new concept. The term itself originated in the 1950s. Arthur Samuel from IBM coined the term based on his research on computer checkers. In a game between a computer and a Connecticut checkers master, the computer won. This outcome opened up a world of possibilities.

Today, machine learning has expanded far beyond simple games of checkers. Although there is still room for innovation, this technology has seen tremendous improvement. And with its many applications, it is moving into the public consciousness as well.

A Quick Overview

Machine learning involves using data to teach computers to learn and improve independently. Its pioneer Arthur Samuel defines it as “the field of study that gives computers the ability to learn without explicitly being programmed.”

It is a field under artificial intelligence (AI), which is the capability of machines to make decisions akin to human behavior.

Scientists can jumpstart the machine learning process in different ways. It can be through data, examples, or system experience. The end goal is for the computer to pick up on the information and learn without human intervention.

SEE ALSO: Data Privacy Day 2022: Tech leaders discuss data privacy and protection in 2022

Who Is Using It?

Machine learning and AI have a pretty broad scope of applications. These past years have especially seen unprecedented growth in this technology.

More and more industries are using machine learning in their operations. In 2021, 57% of companies across different sectors reported AI adoption. This figure is a 45% jump from 2020, implying a rapid adoption rate in businesses worldwide.

Machine learning technology can have boundless applications, as research is still ongoing.

Advertising

Have you ever tried thinking about something you wanted, then the next thing you know, you see an online ad for it? This can be an eerie experience to have, especially if you never posted or talked to anyone about it.

We might assume that companies watch our every move, and we’d be half right. This phenomenon is the result of big data and machine learning.

Websites monitor people’s activities all over the internet, even outside social media. Online activity can reveal vast amounts of personal data. It is this data that advertisers utilize to publish targeted ads.

Finance

The finance sector can be pretty conservative when adopting new technology. This is mainly because of its need for stability and risk hedging. Assessing risks and monitoring trends through patterns like hammer candlestick can be challenging to leave entirely to machinery. However, machine learning has proven to be helpful in ways that do matter.

Machine learning can help financial companies assess risk more effectively through fast-paced algorithms. It can take into account economic volatility and predict future trends. This feature can be handy for strengthening one’s investment portfolio. It allows investors and companies to explore various outcomes. This is an important consideration before settling into a course of action.

It’s also especially helpful in fraud detection. Machine learning algorithms can study each user’s activity patterns. If the algorithm can sense inconsistencies, it would alert the system for potential fraud. Banks and credit card companies often use this system.

Healthcare

Machine learning has proven its applications in healthcare, especially during the COVID-19 pandemic. One of its most prominent uses is computer vision, most famously applied in Taiwan.

AI infrared cameras monitored travelers across different points of entry in Taiwan. The data from these cameras then went to a centralized processor. The processor had machine learning algorithms that picked up on potential COVID-19 symptoms.

This computer vision system allowed Taiwan to contain its infections successfully. This was crucial during the first waves of the pandemic since Taiwan was close to the virus’ hotbed.

Aside from computer vision, machine learning algorithms are especially good at analyzing patterns. Data and statistics are a significant part of healthcare, especially during a pandemic. These algorithms can help doctors and analysts identify specific trends. Such data can be crucial for improved diagnoses and treatment plans.

Transportation and Logistics

Machine learning can also be of great help to the transportation and logistics industry. It can be challenging to optimize travel routes with several factors affecting them. These factors include traffic, environmental factors, customer expectations, etc.

Algorithms can help calculate the most efficient routes for public transport and logistics vehicles. The most accessible example of this is navigation apps.

Navigation apps like Google or Waze use machine learning to predict traffic patterns. The more people use these apps, the better they recognize certain patterns. This helps improve the app’s performance, which, as a result, also assists the user.

Aside from optimizing processes, machine learning can also help in data gathering. Governments can use algorithms to monitor accident rates and identify relevant patterns. This information can be critical to enacting better traffic and safety laws.

SEE ALSO: “A little automation can save a lot of time for the developers”

Final Thoughts

Machine learning technology has a lot to offer, as evidenced by its many applications. It’s interesting to see its evolution through the years. There is still much to learn and discover about machine learning. It is a powerful tool and will require extensive understanding. It is thus essential for various sectors to stay educated about the matter to better harness its benefits.

Author
Gaurav Belani
Gaurav Belani is senior SEO and content marketing analyst at Growfusely. He has more than seven years of experience in digital marketing. He likes sharing his knowledge in a wide range of domains ranging from marketing, human capital management, emerging technologies and much more. His work is featured in several authoritative tech publications. Connect with him on LinkedIn and Twitter at @belanigaurav.

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