What is the Future of Machine Learning?
Machine Learning has been one of the hottest topics of discussion among the C-suite. The blog speaks about the future of Machine Learning. Read this to know more.
With its incredible potential to compute and analyze huge amounts of data, advanced ML techniques are being used in businesses to perform complex tasks quicker and more efficiently.
The machine learning market is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
Machine learning-driven solutions are being leveraged by organizations to improve customer experience, ROI, and to gain a competitive edge in business. Big players in the field like Google, IBM, Microsoft, Apple, and Salesforce are already leveraging ML benefits, when will you?
Hire an ML developer to incorporate ML benefits in your business. Here in this blog, I have discussed the future of ML. Let’s read about these in detail.
Future of Machine Learning!
Machine Learning (ML) is an application of AI (artificial intelligence) that allows systems to learn and improve without being programmed or supervised. If you are keen to know what is the future of Machine Learning, then you can read further to know more.
Being an intensively evolving language, continuous technological advancements are bound to hit the field of Machine Learning which are going to shape the future of Machine Learning. Let’s take a sneak peek into the future of Machine Learning in 2020.
Improved Unsupervised Algorithms!
Improved Unsupervised Algorithms are one of the applications of ML that you can witness in the coming days. Being used in multiple industries, improved unsupervised ML algorithms will certainly shape the future of Machine Learning.
Machine Learning makes use of unsupervised algorithms for analyzing the results. Using these, Machine Learning makes predictions from the datasets when only input data is available without any corresponding output variables.
On the other hand, supervised algorithms work differently. The output of a given algorithm is already known in supervised learning. The unsupervised algorithms work on Artificial Intelligence.
When algorithms are left alone to work on its own, they discover and identify the interesting hidden patterns or groupings within a dataset which would have not been identified by using supervised algorithms.
In the coming years, as the language evolves, more improvements in unsupervised machine learning algorithms can be seen. It’s no wonder to say that this ML application will surely affect the future of ML and result in more accurate analysis.
Machine Learning has been used in mobile app development too. See How Machine Learning Can Revamp Your Mobile App?
Increased Adoption of Quantum Computing!
Increased adoption of Quantum Computing by businesses is one of the major applications of Machine Learning trending now. Quantum Machine Learning algorithms have great potential that can completely transform the future of ML.
“The world is running out of computing capacity. Moore’s law is kinda 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.
Quantum computers when integrated into machine learning leads to faster processing of data. This can help to enhance the ability to analyze and draw meaningful insights from a given dataset.
This increased performance helps businesses to 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 techniques.
Microsoft and Google have already announced their plans to leverage the technology in the near future. With such widespread adoption of quantum computing, it won’t be wrong to consider it as one of the major applications deciding the future of ML.
Enhanced Personalization!
Using Machine Learning algorithms to render enhanced personalization is an important ML application that is worth noticing. Machine Learning personalization algorithms are used to offer product recommendations to customers.
ML algorithms read customers’ patterns and behavior based on which they draw relevant conclusions regarding people’s interests. Companies use Machine Learning techniques to know about a person’s browsing activity on an online retail website.
Using this information, companies send product recommendations such as personalized emails and messages to their targeted prospects. ML techniques help in understanding consumers’ likes and dislikes which keeps them hooked to your services and products.
This is why 82% of marketing leaders are adopting AI and machine learning to improve every aspect of their personalization strategies.
By delivering what and when your customers desire, you can increase customer retention which is what any business aims for. Clearly, improved personalization through ML techniques is the future of Machine Learning.
You can also render enhanced personalization to your users by using an enticing app interface. All that you need is to hire a Reactjs developer who can create interactive web applications for alluring more customers to your services.
Improved Cognitive Services!
Applications these days are becoming more interactive and intelligent than ever before. All thanks to Machine Learning! With the help of cognitive services driven by Machine Learning, applications and devices have become more responsive.
Needless to say, with the widespread use of cognitive services across major industry verticals, it is definitely going to shape the future of Machine learning in the coming days.
Trained on certain patterns, cognitive services allow developers to include intelligent capabilities into their applications. Coders can embed various cognitive features such as visual recognition, speech detection, and speech understanding in their apps using Machine Learning.
As this advanced technology continues to evolve, I am sure the world is going to see highly intelligent applications using cognitive services that are going to decide the future of ML applications across the globe.
Rise of Robots!
Wondering what is the future of Machine Learning. Increased use of robots to carry out business operations will be a major use of ML in 2020. Robots use machine learning algorithms to perform tasks. Since, robots execute tasks in a faster manner, hence businesses across the globe are adopting robotic techniques to increase their productivity.
A Market Research Engine report says that the Global Service Robotics market is expected to reach almost $24 billion by 2022. The market is projected to have a compound annual growth rate (CAGR) of more than 15%.
With continuous developments being made in the field of Machine Learning, we can expect more intelligent robots for carrying out business activities. Increased adoption of Robots by businesses will drive the future of Machine Learning in the tech market.
Similar to the internet, drones are now becoming powerful business tools. They’ve already made the leap to the consumer market, and now they’re being put to work in commercial applications thus creating a market opportunity that’s too large to ignore, says Goldman Sachs.
Machine Learning applications play an important role in businesses these days. To know more about this, read the article to know about the 5 Ways Data Science and Machine Learning Impact Business!
The Bottomline
So discussed above are the top ML applications in 2020 that will shape the future of Machine learning. These top ML forecasts about the future of ML clearly indicates the increased application of Machine Learning across various industry verticals.
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 sure you now have got a clear idea of what is the future of Machine Learning.
