Evolving Trends of Machine learning in 2021

Evolving Trends of Machine learning

Evolving Trends of Machine learning in 2021

Machine learning is evolving and expanding its coverage in the market. See how!

Machine learning has evolved the world at a rapid speed. Currently, devices such as smart home appliances, Amazon’s Alexa, smartphones, and more have adopted Machine learning to serve the target user in a more advanced way. The ultimate purpose is to enable automation and improve the accuracy of the results.

Now with such boosting popularity, Machine learning is expanding its applications into multiple sectors such as e-gaming, defense, finance, and customer service, among others. Technology is helping businesses in improving the quantity and quality of their products or services.

Take the recruitment sector for an example – Covid-19 has caused a huge number of layoffs globally. Therefore, it directly resulted in an increased number of job seekers. Now, with a large pool of applicants trying to apply for jobs, recruiters can face trouble and an overwhelming workload. Now, that’s when Machine learning comes into the picture. Machine learning can help recruiters to execute HR tasks such as scanning resumes in bulk and shortlisting suitable ones, screening candidates, assessing qualifications, and more.

1. Machine learning in the transportation industry

In the transportation industry, Machine learning has been already contributing for quite some time through apps. Among a few top real-life examples of Machine learning, Google Maps is the most prominent one. However, the future is expected to bring a much-advanced route system based on Machine learning techniques. With the help of the factors such as traffic behavior, weather conditions, and more, the ML algorithms will be capable of forecasting the best routes possible to the destination.

Take Valerann’s sensor as an example. Valerann’s network of sensors integrated with roads collects data such as road conditions and shares it with the cloud service. Later, this data is shared with drivers or automated vehicles to inform them about the route conditions.

Also read: How to Create Machine Learning Models into Your Mobile App?

2. Machine learning in Mobile app development

If you have ever used streaming services such as Netflix on your mobile, you might have noticed that after a while of streaming, the homepage gets changed and includes shows or movies relating to your taste. That’s what Machine learning does. With the help of the usage behavior pattern and algorithms, ML helps Netflix in deciding what you might find interesting to watch next. The same thing is done by Facebook as well. Facebook has used one of the best hybrid app frameworks along with the ML concepts to make sure its app is smart.

3. Machine learning in edge devices

With time, companies are focusing on building chips for better performance in edge devices. And, ML is being helpful in it. The purpose is to reduce or end the dependency of AI and Machine learning on the internet. Take Samsung’s Neural Processing Module (NPU) for example. The chip is designed to process thousands of computations parallelly. Tesla has also joined the race by developing an AI chip to support its automated vehicles.

Now, as the chips are small, the power is limited. So, the need of the hour is to focus on ways of boosting the server power so that companies can reduce their dependencies on cloud-based services. Well, we might be able to witness these advanced AI chips soon enough.

4. Machine learning in cybersecurity

2020 saw a 600% increase in cyberattacks. You can blame Covid for it, but it proved how important it is for us to be more concerned about our online security. Now, to tackle such huge numbers, Machine learning can be proved as handy. ML with AI in cybersecurity has helped developers in building effective programs to recognize cyber threat patterns and tackle them.

AI-powered data collection tools can use already existing data on communication networks, financial databases, and websites, among others to identify cyber threats and neutralize or warn about them.

Moreover, AI is also helping users in real-world security with smart ML-supported equipment like CCTV cameras, motion detectors, and more. For instance, AI-supported CCTV cameras are capable of detecting movement even in the darkness and raise an alarm or inform security forces to stop the invasion.

5. Machine learning in software testing

All software has to be extensively tested before it is released. Originally, this was done purely manually, but nowadays applications are so complex they have to be tested automatically. The traditional way to do this was using Selenium, a framework that allows a computer to control your application UI on the screen. You wrote a script telling the system exactly what you wanted to happen. For instance, find the login button and click on it. This worked well 15 years ago, but now Selenium is showing its age.

Modern applications change very quickly, and each time they change, a lot of tests will break because the script can no longer find the item it’s looking for. Machine learning is overcoming this problem in novel ways. For instance, rather than selecting a specific item on the screen, Machine learning software testing systems can create a fingerprint. This fingerprint is built up of lots of things, like where the item is on screen, what items appear with it, what text it contains, its ID within the HTML, etc. The upshot is, machine learning is improving software testing significantly.

Also read: The Expansive Reach of Machine Learning

Key Takeaways

The purpose of this blog was to throw some light on the Machine learning trends that might see a boost in 2021. As 2020 was a year of uncertainties, it triggered the need for changes, especially technologically.

The ML technology has to be foolproof to avoid cyber threats that are not only increasing in numbers but getting smarter. Moreover, as we are working on technologies such as automated manufacturing units or driverless vehicles, these vulnerabilities against cyber threats can be fatal.

And apart from the cybersecurity part, the current need of the hour includes less reliability of the ML on the internet. As we talked about above, with time, the technology has to be more independent so that even small businesses can adopt it without having to worry about huge cloud-based server costs.

But then again, to make an impact on current trends, all it takes is one innovation. Who knows, someone might find a way of Machine learning concepts that we are not even thinking about yet!

Post a Comment