What is Machine Learning and How Will it Expand in 2016?

Nancy Anderson
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Thanks to innovations in computer technology, including cloud computing and data mining, the concept of machine learning aims to make huge strides in 2016. Once-vaunted tech companies lead the way with this technological marvel that lets computer programs learn on their own without any additional programming.

The main driver of machine learning remains the need for IT to expand and grow on past frameworks as computers work with extraordinary amounts of data. Just as IT changed 10 years ago with the advent of cloud computing, new ways of storing data should occur, thanks to better software programs that teach themselves independently as they gain more and more information. Big data — and access to it — leads to the more efficient software needed to process constantly flowing and changing information.

One key to machine learning programs starts with open source coding. Open source allows more programmers to delve into problems and fixes, thereby patching any holes in these programs at a faster pace. Companies should realize the true proprietary information with these programs comes from big data itself as opposed to the methods used to store, retrieve and manipulate the data.

The sheer amount of data out there for tech companies to digest is unimaginable. The rise in jobs for specialists that handle large amounts of information and the security that protects this information made headlines in 2014 thanks to cybersecurity issues. New job titles, new startups and publicly traded companies all need experts dedicated to storing their intellectual property, safeguarding trade secrets and protecting customer information. These new types of IT employees must keep an eye on machine learning trends to make these data systems more efficient moving forward.

Older tech companies, once thought left behind during the mobile revolution, suddenly have new life. IBM, HP and Microsoft have all latched onto machine learning as a way to repurpose their business models. IBM, in particular, moved away from servers for businesses and reinvented itself with Watson, an API that helps businesses access the data they mine. Oracle may be next on the list as the company specializes in databases.

Companies should look for upgrades to Apache Spark, a processing system that makes existing memory programs run up to 100 times faster. Apache Spark works with several types of computer programming languages, such as Java and Python, making it an important piece of software in the coming revolution. This open source code must grow beyond relying on Hadoop's MapReduce program as its driving force so programmers can work on improving other data storage and retrieval software. Once Apache Spark learns to query other database frameworks, companies can tap into this resource to efficiently retrieve large amounts of data continuously.

Machine learning allows computer programs to learn independently, almost like a rudimentary artificial intelligence specially made for data retrieval. When more of these programs hit the market, more businesses can tap into this new paradigm of data retrieval to improve their operations.

Photo courtesy of Stuart Miles at FreeDigitalPhotos.net



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