Machine learning is fueling other technologies to make them smarter. With time, it is updating its algorithms and being the reason for new innovations. In this blog, we are discussing a few possibilities of innovations caused by machine learning we might witness in 2022.
With the help of machine learning, automation has become a possibility for almost every industry. Healthcare, finance, logistics, and more, machine learning algorithms are now becoming a part of these industries to help them in achieving their best possible efficiency. According to a Statista report, in 2021, Newsle lead the global machine learning industry with an 88.71% share of the entire market.
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Machine learning has been fueling industries and making them more efficient as it is getting advanced. The technology is used today to reduce the cost of medicine production by providing possible outcomes to avoid wastage of resources, make automated vehicles more efficient, home security systems smarter, and more. Today, machine learning is used to improve the customer experience the most. To prove that, we are including a Statista report further.
Source- https://www.statista.com/statistics/1111204/machine-learning-use-case-frequency/
As per this report, most usages of machine learning cases are customer-centric. Either the technology is used to improve experiences or collect customer-based analytics. Business owners are also using machine learning for tasks such as increasing brand awareness through targeted ads.
To find the right potential customers, big data is collected. This data can include information of customers like their purchase history, music streaming behavior, job title, income level, and more. Accordingly, businesses use data and predictive analytics to target and approach customers virtually.
Furthermore, in this blog, we are discussing a few crucial innovations of machine learning frameworks and algorithms that we might witness in 2022. So, if you find the topic interesting, stay with us until the end of this blog.
What is machine learning and how is it different from AI?
Before we proceed further and discuss the innovations of machine learning, let’s understand what machine learning is and how it is different from artificial intelligence (AI).
Know more: AI vs. Machine Learning vs. Data Science
To understand in short, machine learning is the process of improving the efficiency of the computer system automatically with the help of big data. To empower AI as a whole system, big data and machine learning play crucial roles.
In other words, machine learning can be referred to as the subset of AI that automatically develops new computer programs and patterns based on the big data it collects or feeds with. Take cybersecurity for instance. With evolving technology, cyber attackers find newer ways of attacking.
The data generated through these attempts can be converted into big data and fed to AI systems. Machine learning algorithms, accordingly, can adjust themselves by taking this big data as the reference to be well-equipped with possible upcoming attacks.
Types of machine learning algorithms:
Machine learning algorithms are usually divided into a few models that you should know about if you want to understand the future of machine learning innovations. Here’s a look into these ML models.
- Supervised machine learning algorithms use labeled data to predict the output accordingly. In other words, you feed the data and the output to the machine as the initial samples. Later on, these algorithms use the pattern to predict future outputs. For instance, you can find predicted house prices of a region in the future with the help of historical data.
- Semi-supervised machine learning algorithms use labeled data and unlabeled data. However, larger amounts of unlabeled data are used to teach machines to predict outputs. There are three types of assumptions that semi-supervised algorithms use to predict outputs:
- Cluster assumption– As the data is saved in multiple clusters, the data saved in a single cluster has a higher chance of being labeled in the same category.
- Continuity assumption- Data points that are closer have a higher chance of getting labeled with the same output.
- Manifold assumption- It uses conditions defining distance and densities on a manifold to predict the output.
- Unsupervised machine learning algorithms use unlabeled data to extract outcomes. The machine learns on its own by trying to find out patterns and hidden clues in the data saved in clusters. These algorithms are able to find similarities and differences to analyze the data accordingly. If you have used services such as Netflix or Spotify, you might have seen their recommendations based on your taste. They are using unsupervised learning algorithms to personalize the catalog based on your usage behavior and patterns.
Innovations of machine learning in 2022
Now that we are done with understanding machine learning in simple terms, it is time to move on and talk about the innovations that we might witness in 2022. So, without further ado, let’s begin!
1. Machine learning in Metaverse
Metaverse is the hottest topic in the technology world at the moment. From Zuckerberg announcing a virtual world that will just work the way the real world works, to Facebook changing its name to Meta- there have been many things since the technology became accessible to the public.
Now, as Metaverse is designed for every individual and business existing out there, without AI and ML algorithms, it would be impossible to manage and secure the huge amount of data that is already processed every second. Let’s discuss the possibilities of ML that we might witness in the Metaverse in 2022-
- Empowering Nonplayable characters (NPCs) with the help of machine learning to give them interactions, storylines, personalities, and more.
- Matching people on virtual platforms just like dating apps do.
- Predicting cyberattack attempts by taking historical data into the consideration.
- Automated storytelling is also not an imagination now. People are already using AI to feed it with big data like thousands of stories or scripts so that it can automatically generate new stories to tell.
- Smart interactions with the help of Haptic gloves can be possible. Users can feel objects that do not even exist and machine learning can improve the quality of sensations that users feel while interacting with a virtual object. However, it also depends on how good haptic gloves can become.
2. Machine learning in smartphones
AI and machine learning algorithms are already coming as basic components of modern smartphones. From supporting cybersecurity protocols to making cameras more efficient, machine learning algorithms are improving the user experience like never before.
You can take the Night Mode for example. AI uses the camera to capture multiple layers of images if the Night Mode is selected to click photos. Then after merging these layers into a single image, AI processes them to correct colors, sharpen them better, and more. In 2022, iPhones are also planning to use machine learning algorithms to block third-party apps and services from tracking your user activity.
The modern A12 chip uses machine learning algorithms to power up the processor for improved performance. On top of that, it is also enabling more efficiency for fun features such as Animoji that can now read facial features better and react accordingly.
Machine learning is also used to fasten up the speed of the face recognition feature that unlocks smartphones. With the help of big data, it is also being ensured that objects like power glasses do not become the hurdle in unlocking an iPhone using facial recognition.
3. Machine learning in robotics
Sem-Automated robotics traditions are already existing in many industries such as operational, shipping, and more. However, machine learning is now enabling these robots to see and make decisions entirely on their own. Take Tesla’s automated vehicles as an example.
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With the help of AI-supported vision, these vehicles are able to predict and avoid possible accidents. Moreover, robots are also seeing an expansion in the healthcare industry.
For instance, to perform delicate surgeries, trials of robotics are being done to perform delicate surgeries. The purpose is to reduce the probability of any humane errors while performing surgeries that require the utmost precision.
4. Machine learning in the intelligent gaming
Machine learning is being used by game developers to put lives into Non-playable characters, provide smart interactions, and more. Top titles such as Red Dead Redemption, Minecraft, Chess, Dota-2, and many more titles have used machine learning algorithms to fuel their games.
In 2022, the gaming industry is also looking forward to using machine learning algorithms to detect cheaters and hackers.
For instance, PUBG has upgraded its cheat detection system to detect hackers using hacks like flying, auto-aim, and many more. From the server perspective, machine learning is also able to protect game servers from any kind of hack attempts done by hackers or rival companies.
5. Machine learning in personalized education
Education is still following traditional methods of learning. However, many countries are also experimenting with the benefits of using machine learning for the education sector. With the help of machine learning, the learning pattern of a student can be tracked to figure out their weaker and stronger sections.
Accordingly, a tailored learning process can be developed to assist each student better. Machine learning can also help in reducing the pressure that hectic ways of learning create. It can make virtual assistance more accessible so that students have the benefit of on-demand help.
Read more: How the Application of Artificial Intelligence Assists Businesses?
For teachers and authorities, machine learning can be helpful in preparing the best possible syllabuses that are more effective compared to the traditional ones. These syllabuses can either be created manually or automatically with the help of machine learning by using the data that AI analytics collect.
6. Machine learning with Augmented Reality (AR)
Augmented Reality (AR) is an evolving technology. 2022 can be the year when we see this technology adapting machine learning for improved efficiency. Currently, the technology is being used for gaming, interior designing, virtual trials, and more. But pairing up AR with machine learning can help in other sectors such as education. How? Let’s look into that.
For instance, with AR and ML, students can make choices of medicines or treatment patterns to find out the possible output.
Moreover, it can also give an almost impactful practical experience which will make even harder principles to get memorized easily. For studies of astronomy, AR and ML can predict locations of planets, stars, and more to display data accordingly.
Curiscope’s Virtuali-tee can be taken as an example of modern AR usage in the education sector. The technology is used to help users in learning about human anatomy. Paired with an app, the t-shirt can be worn to reveal and explore different layers of the human body on its app. The tool is designed to educate people.
7. Machine learning in fintech apps
Modern banking apps allow you to record your spending activities so that with machine learning these apps can help you in making effective financial decisions.
On top of that, for faster responses at any time of the day or year, these fintech apps will also fuel chatbots with machine learning so that you can have their help without any involvement of a human agent.
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These chatbots will solve queries much faster and make fewer errors while reducing the cost for banks in parallel. Even currently, chatbots are able to solve up to 70% of queries without needing any help from a human agent.
This might cross 95% of the threshold in 2022 as customer service industries are working tirelessly to make these chatbots fully automated.
Not only that, but 2022 might also bring improved security protocols for fintech apps as new types of attacks are emerging into the market.
From blocking impacts of any hack attempts done on smartphones to protecting user credentials from being tracked or copied, these machine learning algorithms are getting smarter to provide enhanced security protocols.
Conclusion:
Well, these were the points that we had to discuss to explain to you a few innovations we might witness in 2022. However, there are some uncertain innovations that we might also see like improved quality in the future of the app industry with machine learning.
2022 is a year when the world is resuming many pending tasks that Covid-19 had slowed down. Take the expansion of the 5G technology for example.
Thus, we might witness newer use cases of machine learning technology that we can not predict yet – for instance, Metaverse. In the end, we hope that you found the blog informative. For any more information and to read more amazing tech blogs, keep exploring the website.
Author bio:
Andrea Laura is a very creative writer and active contributor who love to share informative news or updates on various topics and brings great information to her readers. Being writing as her hobby, Andrea has come out with many interesting topics and information that attracts readers to unravel her write-up. Her content is featured on many mainstream sites & blogs.
