Key trends in 2017 for machine learning

Machine learning has traveled from the labs to applications which we are using today. Though still in its later infancy stages, machine learning evolution and adaptation has been the talk of the technology industry.

Key trends in 2017 for machine learning

Key trends in 2017 for machine learning

Machine learning has traveled from the labs to applications which we are using today. Though still in its later infancy stages, machine learning evolution and adaptation has been the talk of the technology industry.

With Google, Facebook, Tencent and other worldwide technology leaders taking giant strides in the field of machine learning. Technology retail based organisations have already started employing machine learning to not only handle their customer services but they have now started using this bold technology to better understand their back-end inventory processes.

There are a few emerging key trends within the machine learning sphere, with industry leaders calling it the next big data.

With the help of this blog post we would try to shine light on the new emerging facets of machine learning and how it would impact the average user.

First and foremost is the mobile technology and applications domain. The modern day applications built under the tutelage of machine learning are now only going to be smarter but they would also have built in features to better understand the evolving user demands.

The modern day application would not only troubleshoot but also indulge in real time predictions and improvisations within its processes to serve the customers in a better and effective way.

These intelligent apps would be working towards delivering better experience for the end users and customers 24×7.

With the advent of microservices, machine learning would enable the service providers to learn and improve their intelligent apps instead of making corrections to the code itself. The new demands would be catered to instantaneously instead of taking place as part of app upgradation which consumes precious time.

This would also mean that the machine learning micro intelligent apps would be consuming more data to provide a better seamless experience to its users.

Several digital assistants like Cortana, Siri and Google Now are already spearheading this revolutionary step up in modern day technologies..

Another key trend which falls in the ethics chapter of these technologies would be on how to maintain trust and transparency throughout.

We all live in a world today marred by online surveillance and backdoor technologies. And, frankly none of us would like to devote their time in understanding how they can trust their intelligent applications.

Instead, the onus lies with these service providers and organisations to build in a niche of features which would lay the stepping stones for user privacy right from the outset.

Understanding the “why” and “what” in this chapter would firmly remain a responsibility which the technology organisation would have to look and re-look into often.p>

The other trend is end user suggestion or recommendation modification which companies so often use to target their customers. Though the success rate of machine learning technologies is better than normal search strings employed by the customers. Yet, some companies like Redfin are utilising human based suggestion and recommendation reviews to add the human learning ability to its machine learning facet.

This would mean that judgement and proactive corrections can be learnt by the intelligent algorithms which are busy monitoring the usage of the intelligent apps.

Last but not the least, machine learning is never the step one in today’s intelligent apps market. It comes after a unique UI/UX module has been developed and the machine learning architecture is being used as a support system complimenting theses foundations.

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