The Future Of Machine Learning: 3 Major Trends To Watch Out
Jul 13,2016

To understand that our machines are getting smarter in learning a few things from our actions we do not need to resort to futuristic inventions like the driverless cars. Well, it is already there and we are using it every day. Before you raise your brows, let me tell you that your own search engine is a good example of how machines learn from you.

Google algorithm learns from millions of searches and searches behavior types every day. Similarly, the Facebook algorithm detects suspicious activities by learning from our own posts and behavior over there. They make mistakes as well often by failing to exclude typical cases. These typical and exclusive cases make the gray area for any machines to learn and handle like we human do. While machine learning will only evolve to get stronger and wider in scope we can easily make out these 5 trends. Let us have a look at them.

Data-Driven Machine Learning

Data-Driven Machine Learning

We are already living in a connected reality with fluid interaction among our gadgets and interfaces. So what, how can it change the way machines interact with me? Wait, did you ever figure out all the information that you generate once and for all? Every piece of data, from your train booking to electric bill to store invoice to chat messages, all contain vital insights about your web and app behavior, timing, preference and so many things.

Powerful data analytics tools help drawing insights and to feed your algorithms with them. Thus, at regular intervals machine interfaces are getting further easier for our use. In the time to come, data analytics will be part of any machine algorithm allowing it to behave more intelligently as per the user profiles.

Gadgets Learning User Contexts

Gadgets Learning User Contexts

Thanks to location technologies machines most of the times know where you are. Similarly, advanced data analytics allow machines know the ‘when’ and ‘how’ factors of your preference. These factors representing ‘where’, ‘when’ and ‘how’ elements of the user make the context. Already your mobile device understands a lot about your context and responds accordingly. With the web-ready television, car computers and app-driven home automation systems, soon more gadgets will learn and behave as per personalized user context.

Security Breaches Continue To Be A Big Concern

Security Breaches Continue To Be A Big Concern

As the number of connected devices increase, personal information will be subject to more exposure. This is likely to create more security threats including data thefts, intrusion, unsolicited data usage, malware attacks, etc. While the race to innovation will continue, IoT device manufacturers are not responding with security arrangements that the new breed of connected devices requires.

In the recent times, we have seen baby monitors and car computing systems being hacked. With an increasing number of devices participating across the all-encompassing IoT platforms, security of personal data in individual devices is often getting overlooked and ignored. With the innovation driving IoT platforms and machine learning, we can expect more security glitches in the time to come.

So, do these trends look threatening or promising?  Well, machine learning is an outcome of connected reality and we cannot take it in any other way except positively. Like any new technology and digital interface it is bound to have some risks concerning data sharing and data access, but over time such vulnerabilities will be addressed to the hilt.



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Showing 8 Comments

  1. Shrestha Tripathi

    Machine Learning will proliferate the mobile market and enter the territories of drones and self-driving cars. Gartner’s Ian Bertram predicts that more domain-specific and Machine Learning-enabled technologies will emerge this year. Democratization of AI/machine learning will continue, according to Mark Koh. The demand for making algorithms more easily available will push vendors to offer many new Machine Learning tools.

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