The Future of Machine Learning

Currently, machine learning has been one of the hottest topics of discussion among the C-suite. Hence, let XRCLOUD provide more information about machine learning and the future of machine learning!

Advanced ML techniques are used in organizations to perform complex tasks faster and more effectively, with their immense ability to calculate and analyze large quantities of data.

At a Compound Annual Growth Rate ( CAGR) of 44.1 percent during the forecast period, the machine learning market is projected to rise from USD 1.03 billion in 2016 to USD 8.81 trillion by 2022.

Machine learning-driven solutions are being leveraged by organizations to improve customer experience, ROI, and gain a competitive edge. Big players in the market, such as Google, IBM, Microsoft, and Apple, are already leveraging ML benefits. Thus, when will you or your business start to do such action?

Machine Learning ( ML) is an AI (artificial intelligence) technology that enables systems, without being programmed or supervised, to learn and develop.

Being an intensively evolving language, continuous technological advancements are bound to hit the field of Machine Learning, which will shape the future of Machine Learning. Let's take a sneak peek into the future of Machine Learning in 2020.

Improved Unsupervised Algorithms!

Improved Unattended Algorithms are one of the ML applications that you will be able to see in the coming days.  Improved unsupervised ML algorithms would undoubtedly shape the future of Machine Learning when used in multiple industries. Machine Learning makes dataset predictions when only input data is available without any associated output variables. Supervised algorithms, on the other hand, function differently. In supervised learning, the performance of a given algorithm is already known. Artificial Intelligence is the work of unsupervised algorithms.

When algorithms are left alone to work independently, they discover and identify the interesting hidden patterns or groupings within a dataset that would not have been identified using supervised algorithms. As the language evolves in the coming years, more improvements in unsupervised machine learning algorithms can be seen. It's no wonder to say that this ML application will affect ML's future and result in a more accurate analysis.

Increased Adoption of Quantum Computing!

Companies' increased acceptance of Quantum Computing is now one of the main applications of the trend in Machine Learning. There is tremendous potential for Quantum Machine Learning algorithms that can altogether change the future of ML. "The world is running out of computing capacity. Moore's law is running out of steam … [we need quantum computing to] create all of these rich experiences we talk about, all of this artificial intelligence, said Satya Nadella, Microsoft CEO.

When implemented into machine learning, quantum computers result in the faster data processing. This will help to develop the ability to evaluate a given dataset and draw useful conclusions from it. Further, it also increased performance helps businesses achieve great results that were not possible using classical ML techniques. Companies are now trying hard to harness the power of quantum computing to create more effective strategies. 

Microsoft and Google have already revealed their intentions for the technology to be leveraged shortly. With such widespread acceptance of quantum computing, it would not be incorrect to regard it as one of the significant applications determining ML's future.

Improved Cognitive Services!

Applications these days are becoming more interactive and intelligent than ever before. All thanks to Machine Learning! It has become more sensitive with the assistance of cognitive resources powered by Machine Learning.

It will shape the future of Machine Learning in the coming days, with the widespread usage of cognitive resources across major industry verticals. Cognitive resources, trained on specific trends, allow developers to integrate intelligent capabilities into their applications. Coders may integrate various cognitive features, such as visual recognition, speech detection, and speech comprehension, into their applications using Machine Learning.

As this advanced technology continues to evolve, I am sure the world will see highly intelligent applications using cognitive services that will decide the future of ML applications across the globe.

The Bottomline

With the aid of Machine Learning, cognitive services, software, and devices have become more receptive. Gartner predicts that by 2022, 75% of new end-user solutions leveraging AI and ML techniques will be built with commercials instead of open-source platforms.

After reading this article, I am pretty sure you now have a clear idea of the future of Machine Learning.


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