Are We Using Machine Learning to its Full Extent?

We’re in the information age, and as databases continue to grow, so does our understanding of data, and of what we can do with it. Machine learning embodies this framing. Shifting through vast collections of data to perform impossibly speedy analyses to provide insights we might never have seen with the naked eye is what machine learning is all about. It sounds like fiction, but it’s quickly becoming a standard tool across industries.

However, studies show that adoption of machine learning is still not quite as uniform across businesses as some might expect. It’s a familiar term, but a slightly less familiar implementation in practice. As the trend continues, though, one question stands out: is using machine learning enough? Can we use it better? And, of course, are we using machine learning to its full extent?

The Future of Machine Learning

In its most essential form, machine learning is a subset of artificial intelligence that revolves around training algorithms to make predictions and recognize patterns. Show the algorithm a million pictures of a cat and it will learn how to recognize a cat. Show the algorithm millions of points of market data and, hopefully, it will recognize patterns in the market to help you make important decisions.

This is the essence of what machine learning is and what it always will be. What we can expect to change in the future is not what it is, but how we approach and facilitate the process. It’s easy to say “show the algorithm millions of data points,” but doing so is much more nuanced. A major challenge of data analysis is that data is rarely found in clean, uniform formats, which can make it very tricky to feed any algorithm large amounts of information. However, machine learning itself has been employed in efforts to clean this kind of data and make the “learning” process more streamline.

In similar veins, the capacity for facilitation of machine learning will provide great opportunities for businesses wanting to implement this technology moving forward. Faster and more powerful computers and access to more powerful and robust cloud resources will help machine learning become a more widespread and accessible tool that can help refine and automize different processes that otherwise take up valuable time and effort.

Industries to Keep in Mind

There are a few specific industries that are favorites to benefit largely from further machine learning adoption and implementation. A big one of these is the healthcare industry. Just as we mentioned with messy data, healthcare is a prime example of the damages and limitations of too much data in too many formats that make valuable information difficult to access. The ability to clean up these databases quickly and more-or-less automatically could be a huge boost to the efficiency and quality of healthcare.

Another way machine learning is set to help in the healthcare industry is by using its power to predict. As things stand, disease prediction uses a relatively small number of factors that only permit relatively broad strokes predictions. With machine learning computational power, prediction can be much more robust and personalized, and can help care for many more individuals before they become unwell at all.

Manufacturing is another huge industry that is bound to be impacted. From increased quality in monitoring processes to increased capacity and precision in automation, the further implementation of machine learning in manufacturing will do wonders for quality control and scalability.

The Future of Business Information

Strategy is not just about leadership and organizational philosophies. Most businesses recognize that analytics are a core pillar to being competitive, and that makes business information an essential resource to any and every company. How quickly and efficiently that information can be analyzed and leveraged can make all the difference in the market, and with the implementation of machine learning, this analysis can practically happen in real time.

Other ways businesses will look to use machine learning to its fullest extent will be applying this technology to processes that can be automated or assisted algorithmically. Customer assistant chatbots are an example of this AI tech already widely implemented. In other words, the way we move towards machine learning in its “full potential” won’t be a shift in what machine learning looks like, but a shift in where we use it. Faster machines and smarter processes will be what transforms the horizon of business in the age of artificial intelligence.