The Future of Machine Learning: Trends to Watch Out For

The future of Machine Learning has been a trending topic of discussion among C-suite executives around the world — for good reason. Machine Learning brings to the table an incredible potential to compute and analyse massive amounts of Big Data using advanced techniques. This, in turn, allows businesses and individuals to perform complex tasks and processes more efficiently.

Today, Machine Learning is being implemented globally by businesses — large and small. In fact, in recent times, IT giants like Google, IBM, Apple, and Microsoft have leveraged the benefits of ML to drive solutions that significantly improve customer experience and increase ROI.

There’s no doubt that the Artificial Intelligence and Machine Learning industries are growing like never before. And, with the surge in demand and interest, new transformation trends and patterns are taking shape. In this piece, we explore what’s in store for the future of Machine Learning and the different courses that may, in all likelihood, reshape our economic, social, and industrial operations.

1. The era of hyper automation

A recent study conducted by Gartner shows that hyper automation has grabbed the first place in their list of top 10 strategic technology trends that will drive disruption and opportunity over the next decade.

In a nutshell, hyper automation is the technology that implements Artificial Intelligence and Machine Learning to automate processes across a range of applications.

A classic example of hyper automation is an airplane. Today, modern aircrafts apply a combination of GPS, motion sensors, and computer systems to track their position during flight. Simply put, it’s all automated. In fact, an average Boeing 777 pilot spends only seven minutes flying the plane manually, and even that is usually only during takeoff and landing.

On similar lines, the future of Machine Learning in 2020 and beyond involves hyper automation in self-driving cars. Truth be told, these are already a reality, considering the hundreds of safety studies undertaken across the globe. Google Maps and navigation systems have made it possible to compare a device’s location from one point in time to another — determining how fast the device is traveling in real time. By combining that data with users’ incidents, one can build a picture of the traffic at any given instant.

However, when it comes to the actual driving of the car, Machine Learning takes over. Machine Learning allows self-driving vehicles to immediately adapt to changing road conditions by constantly parsing through a stream of sensor and visual data. This allows onboard computers to make split-second decisions — sometimes even faster than well-trained drivers with years of experience under their belt. And we’re going to be seeing a lot more of that in the years to come!

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