FOLLOW

FOLLOW

SHARE

Are We Approaching the Golden Age of Machine Learning?

Machine Learning Booms

24Nov

In much the same way that big data analytics has taken the world by storm in the past few years, machine learning is poised for a breakout period of its own. Machine learning is not necessarily a new concept - far from it, actually - but it has only recently begun to gain traction among many within the tech community and business world. Part of that is the incredible developments that have been made in machine learning algorithms as programmers and data scientists have only begun to unlock the true potential of this technology. And there’s no denying the hype behind it. Gartner’s latest Hype Cycle Report of emerging technologies shows machine learning is prominently on the list, basically taking the place of big data entirely. Many experts say we are on the verge of a golden age when it comes to machine learning. While there may be some dispute over that statement, many signs seem to be pointing to it.

Perhaps it should come as no surprise that machine learning has become so popular in such a short amount of time. Businesses have clearly seen the benefits that come from adopting machine learning technology. Many industries, including insurance and banking, are particularly suitable for using machine learning tools to gauge trends and find hidden patterns among vast amounts of data. If anything, machine learning can be looked at as the natural extension of big data analytics. The benefits of using big data for businesses have been well documented, with many companies crediting analytics for helping them achieve new levels of success. With machine learning often perceived as the super-charged version of analytics (though it’s not an entirely accurate description), the focus has shifted to machine learning technology and tools.

None of this rising popularity, however, would be possible without recent technological improvements. This goes beyond the improved machine learning tools that are now available to many businesses the world over, although that is certainly a significant development. When it comes to improving technologies that make it possible for machine learning to prosper, the first place to look is the spread of scalable computing power. Machine learning requires an immense amount of computing power to function correctly, a level that simply wasn’t possible years ago. Now, in part due to the rise of the cloud, that computing power is more within reach than ever before. This is particularly important for smaller companies that may have more limited resources and funds. Even during times when IT budgets are slashed, the cloud makes machine learning a viable option. The increased popularity of flash storage is also worth pointing out, since its faster performance allows for the quick processing and storage of large amounts of data often found when machine learning it used.

There’s another reason machine learning is ready to reach new heights, one that isn’t often talked about. As useful as big data analytics is, it still has certain limitations that hinder further improvements within an organization. Big data, for example, has gone beyond simply being big - it is massive. The amount of data generated each year is enormous in scope and complexity, and that’s one trend that won’t be going away anytime soon. That makes statistical analysis of the more traditional sort more difficult or even impossible. In other words, due to the amount of information out there, the traditional approach simply doesn’t cut it. To gain the insights promised through big data analytics, companies need to employ machine learning instead.

Organizations also have a far better idea of how to use machine learning than they did years ago. Back then, machine learning was a tough concept to grasp, one with a lot of interesting ideas, but no solid use cases backing it up. That has all changed recently. Now, there are numerous use cases that show how applicable machine learning is, giving businesses a concrete idea of what to do. Cyber security is just one area where machine learning is found to excel, but there are many more. In some ways, that’s just scratching the surface of machine learning’s potential. More uses will be discovered and more potential unleashed as we enter this golden age of machine learning. In many ways, it’s a necessary and unavoidable evolution, but one that businesses stand to greatly benefit from.

Comments

comments powered byDisqus
Glassessmall

Read next:

Working At The Boundaries Of Aesthetics And Inference

i