Machine Learning Becomes Legit, but Not Mainstream in 2017

ML-Sinequa-Predictions-2017

There has been a lot of hype around machine learning lately. Over the past decades, we’ve heard about various concepts around machine intelligence that in most cases didn’t get anywhere. But more and more frequently, organizations are learning how to bring together all the ingredients needed to leverage machine learning, and there is a simple reason for that: according to Moore’s law, the performance of microprocessors has increased since 1980 be a factor of more than 16 million! A program that ran on a 1980 computer for more than half a year today delivers its results in one second!

That is why I think Machine Learning will be the story for 2017. We’ll see it move from a mystical, over-hyped holy grail, to more real-world, successful applications. Those who dismiss it as hocus-pocus will finally understand it’s real; those who distrust it will come to see its potential; and companies that apply ML to appropriate use cases will achieve real business benefit without the high cost of entry that was common in years past. In 2017 it will be clear that it has a credible place in the business toolkit.

The four necessary enablers for machine learning – huge parallel processing resources, cheap storage, large and appropriate data sets, and accessible machine learning algorithms – are all now mainstream. Most large organizations have readily-available access to all these components (appropriate data sets are potentially the only open question, as they are always business- and use-case-specific), which makes machine learning a real possibility to reduce risk, increase customer satisfaction and loyalty, create new business models, identify patterns, and optimize complex systems.

One area where machine learning is growing rapidly and already showing success is for cognitive search and analytics applications. It won’t take over core algorithms anytime soon, but ML is already supplementing and enhancing search results based on user actions and smart analysis of content.

I don’t foresee machine learning achieving “mainstream” status in 2017, but it will within the next few years because the technology is advancing exponentially, quickly enabling its use in broader contexts.

For more on my complete prediction on machine learning, check out this article in Virtual Strategy Magazine.

 

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