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Summary- Machine learning trends are continuously evolving and bringing new innovations into existence. Now, as 2022 is a year of resuming innovations that were paused due to Covid, it is time to see what we can expect from these ML trends this year.

Among all modern technologies, Machine Learning could be the most influential technology that the 21st century has adopted. The technology has helped business owners and entrepreneurs in understanding the market better and unlocking enhanced efficiency for their business operations. Machine learning has also boosted the quality of services and offered improved security, accuracy, and performance.

Now, as time is passing, machine learning has been evolving as well. Today, there are multiple machine learning trends existing across markets that vary depending on the requirements of the industry segment. In this blog, we are shortlisting a few machine learning trends that we think might take over the market in 2022. So, stay with us until the end and have some crucial insights into machine learning trends.

1. Unsupervised machine learning

Unsupervised learning, one of the fastest-growing types of machine learning algorithms, is often used to offer enhanced personalization to users. As the name suggests, the trend does not require any supervision from data scientists. Instead, it uses unlabeled data to find patterns and conclusions on its own. Giants such as Amazon or Netflix are already using unsupervised machine learning to offer better personalization to their users. The big data collected through the usage behavior of the user is used to feed machine learning systems. And accordingly, these systems find patterns and draw conclusions. In 2022, unsupervised learning might see a boost in its popularity to tackle continuously evolving cyberthreats and to join more digital products to offer better quality customization.

2. AI ethics

As AI is expanding its coverage across multiple industries, figuring out and applying proper ethics of the technology has become important too. For instance, as machine learning is dependent on the usage behavior, businesses using AI for such customization will have to make decisions to protect user privacy as well. Even new updates in Android and iOS versions are providing users with options where they can control if they want targeted ads or not. If they switch off targeted ads, no user activity will be tracked by businesses like Amazon to empower their machine learning algorithms. Some other industries such as driverless vehicles, automated trading, etc are also expected to apply proper AI ethics across their services to keep fair competition alive.

3. Natural language processing

Developers and data scientists are continuously putting efforts into improving the naturalization of responses that chatbots provide. Also, machine learning is helping businesses in deploying smart chatbots that can understand multiple languages, accents, and pronunciations. These chatbots are capable of solving user queries through texts, emails, or calls and a report by SmallBizGenius says that approximately 80% of customer service queries are handled by chatbots as of the end of 2021. In 2022, expectations are these chatbots will become more affordable for even small businesses and entrepreneurs which will eventually lead to growth in the normalization of chatbots supported by machine learning.

4. No-code or low-code machine learning development

There are machine learning frameworks existing that allow you to develop machine learning algorithms even without having to write a single line of code. These tools support drag and drop options and are often cheaper in terms of development costs too. For small businesses and entrepreneurs, these models can be the best pick as they require fewer budgets, a very small team, easy deployment, and testing. However, no-code ML development tools can have their own drawbacks too. For instance, very less customization.

These tools include already prepared features that the developer can drag and integrate into projects. However, for extra customization that does not exist in the framework, these tools might not be helpful. The best thing about these tools is that you can enter questions and build patterns by using simple English and there are many tools available in such frameworks that can help you out in building smarter analytical tools with machine learning for various industries such as retail, and finance, research, etc.

5. Metaverse

Metaverse, since its announcement, has been a hot topic among tech enthusiasts and businesses. The technology leverage other technologies including blockchain, AI, machine learning, AR/VR, and Haptic

Gloves. To simply understand Metaverse, we can define it as a virtual universe where users can create their own virtual replica to explore, hang out with friends, play games, and do shopping. Everything is done through VR glasses and the user does not even have to leave the room to experience services on Metaverse.

Now, as Metaverse is quite popular and offers services like Haptic responses so users can even feel virtual objects with the help of Haptic gloves, machine learning plays a crucial role. Machine learning helps in providing accurate responses, securing Metaverse servers, scanning servers to keep them free from bullying or harassment, etc. In short, machine learning contributes to supervising Metaverse better to make its services more efficient and improve the cybersecurity quality.

6. Creative machine learning

As the name suggests, this machine learning trend is existing to support the creation of different kinds of arts. Music, paintings, photography, and more can be supported by machine learning algorithms. These algorithms use historical data to learn arts depending on their goals. For example, to improve photographs, modern apps such as Lightroom or Adobe Photoshop are using AI and machine learning. These tools have eliminated the process of having to select the backgrounds of subjects manually.

Instead, machine learning can help AI in detecting the subject in a picture and selecting it with a single click. Just like that, new areas of machine learning’s creativity are also being explored. For example, the “Netflix is a Joke” YouTube channel uses AI and machine learning to create short animated movies. Some other apps have also started offering features that allow users to colorize vintage pictures, sharpen blurry images, convert still images into animations, etc.

7. Hyperautomation

As the name gives it away, hyper-automation basically refers to a process of applying automation across almost all segments of an organization. Machine learning is deployed to enable this trend across several processes such as research, basic decision making, machine deployments, machine handling and maintenance, cybersecurity, shipments, etc. For institutions involved in processes related to Nuclear or other radioactive wastes, hyper-automation can save plenty of human workers from the radiation risks. Hyperautomation also reduces the possibility of inside breaches or cyberattacks done by employees of an organization from the inside.

8. AutoML

AutoML reduces the dependency on data scientists and automates the process of labeling the data and extracting outcomes. Developers can use resources provided by AutoML tools in the form of templates. These templates allow automation in preparing neural networks that can support any type of program built by developers. Developers can use AutoML to save time and resources that they have to invest in building fully-fledged AutoML systems. These tools also reduce the risk of human error and as costing is saved too, for small businesses and small teams of developers, AutoML can be considered the perfect choice. The crucial difference between AutoML and No-code ML development is that No-code development frameworks offer drag and drop features along with very little customization. AutoML, however, can be used to prepare customized sets of machine learning models for any requirements whatsoever.

Wrapping up

As Covid had slowed down the world for almost a couple of years, 2022 is a year when many paused innovations will be resumed. So, predicting all machine learning trends that might come into existence this year can be quite tricky. However, these trends that we discussed above have a high probability of becoming the new normal, so if you are a data scientist, or planning to learn machine learning development, you might want to consider learning skillsets that may benefit from such trends.

In the end, hopefully, you found this blog on machine learning technology trends that will impact businesses in 2022 useful. If you are curious to learn more about tech trends, you can explore NextoTech to have some amazing insights through tech, marketing, and design-related topics. We will see you with another blog soon, until then, keep reading!

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