Machine Learning - No Longer Futuristic


The time when machine learning seemed a bit too futuristic is long gone now. Thanks to the latest technology trends and mainly thanks to big data, which is now used by many companies worldwide, the future of machine learning looks brighter than ever.

According to a recent article by JavaWorld, there are four ways how machine learning will change/improve in 2016:

  1. Tech dinosaurs will continue to remake themselves around machine learning the way they did with the cloud.
  2. Apache Spark will unshackle itself even further.
  3. Machine learning must be open source by default from now on, no matter what form it turns up in.
  4. The struggle for data sources to feed machine learning will become all the more heated.

Various companies like IBM, HP and Microsoft can now use machine learning and in a way ‘reinvent’ themselves after cloud’s big wave of publicity began to fade away and was no longer useful to the same extent is was some time ago. Big data is helping many companies to improve the way they function and help their businesses do better in the long term.

“IBM in particular has shed its dead-end and no-go businesses (commodity PCs and servers) to make room for all the things big-data-empowered machine learning makes possible. Watson, its machine-intelligence-as-a-service platform, has gone from being a PR stunt to something that promises real utility for businesses, thanks to its public API set,” according to JavaWorld which also suggests that Oracle, as a database company, is going to be the next one to benefit from machine learning. Apache Spark is also going to make machine learning an important part of its core, allowing itself to become even more powerful, faster, and easier to use. According to JavaWorld, there is a big chance that future versions of Spark will also gain a lot from applying machine learning.

“Notably, Spark is continuing to grow on its own, away from the Hadoop big data framework where it rose to prominence. Hadoop's just one of the many data sources that Spark can use, and while machine learning needs lots of data to work well, there's nothing that says Spark has to rely on Hadoop to get it,” JavaWorld explained while emphasized that machine learning is allowing room for algorithms to work way better when they are ‘open by default’, a move that will not only make it easier to work with algorithms, but it will also allow products to have a full transparency.

The 33rd International Conference on Machine Learning (ICML 2016) will be held in New York, USA from June 19 to 24, 2016. Supported by the International Machine Learning Society (IMLS), the conference will consist of tutorials, conference sessions, and various workshops. 




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